bcecd73fdd
the filter tests Double objects by references whereas it should test their values Author: Dariusz Kobylarz <darek.kobylarz@gmail.com> Closes #3081 from dkobylarz/master and squashes the following commits: 5d43a39 [Dariusz Kobylarz] naive bayes example update a304b93 [Dariusz Kobylarz] fixed MLlib Naive-Bayes java example bug
128 lines
5.1 KiB
Markdown
128 lines
5.1 KiB
Markdown
---
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layout: global
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title: Naive Bayes - MLlib
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displayTitle: <a href="mllib-guide.html">MLlib</a> - Naive Bayes
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---
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[Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) is a simple
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multiclass classification algorithm with the assumption of independence between
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every pair of features. Naive Bayes can be trained very efficiently. Within a
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single pass to the training data, it computes the conditional probability
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distribution of each feature given label, and then it applies Bayes' theorem to
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compute the conditional probability distribution of label given an observation
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and use it for prediction.
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MLlib supports [multinomial naive
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Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes),
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which is typically used for [document
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classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html).
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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.
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Feature values must be nonnegative to represent term frequencies.
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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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sparsity. Since the training data is only used once, it is not necessary to cache it.
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## Examples
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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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, and output 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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{% highlight scala %}
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import org.apache.spark.mllib.classification.NaiveBayes
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import org.apache.spark.mllib.linalg.Vectors
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import org.apache.spark.mllib.regression.LabeledPoint
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val data = sc.textFile("data/mllib/sample_naive_bayes_data.txt")
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val parsedData = data.map { line =>
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val parts = line.split(',')
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LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
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}
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// Split data into training (60%) and test (40%).
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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)
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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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{% endhighlight %}
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</div>
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<div data-lang="java" markdown="1">
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[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements
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multinomial naive Bayes. It takes a Scala RDD of
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[LabeledPoint](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) and an
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optionally smoothing parameter `lambda` as input, and output a
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[NaiveBayesModel](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html), which
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can be used for evaluation and prediction.
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{% highlight java %}
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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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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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final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
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JavaPairRDD<Double, Double> predictionAndLabel =
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test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
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@Override public Tuple2<Double, Double> call(LabeledPoint p) {
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return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
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}
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});
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double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
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@Override public Boolean call(Tuple2<Double, Double> pl) {
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return pl._1().equals(pl._2());
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}
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}).count() / (double) test.count();
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{% endhighlight %}
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</div>
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<div data-lang="python" markdown="1">
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[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial
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naive Bayes. It takes an RDD of
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[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally
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smoothing parameter `lambda` as input, and output a
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[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be
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used for evaluation and prediction.
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<!-- TODO: Make Python's example consistent with Scala's and Java's. -->
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{% highlight python %}
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from pyspark.mllib.regression import LabeledPoint
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from pyspark.mllib.classification import NaiveBayes
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# an RDD of LabeledPoint
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data = sc.parallelize([
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LabeledPoint(0.0, [0.0, 0.0])
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... # more labeled points
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])
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# Train a naive Bayes model.
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model = NaiveBayes.train(data, 1.0)
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# Make prediction.
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prediction = model.predict([0.0, 0.0])
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{% endhighlight %}
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</div>
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</div>
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