* Add GradientBoostedTrees Python examples to ML guide * I ran these in the pyspark shell, and they worked. * Add save/load to examples in ML guide * Added note to python docs about predict,transform not working within RDD actions,transformations in some cases (See SPARK-5981) CC: mengxr Author: Joseph K. Bradley <joseph@databricks.com> Closes #4750 from jkbradley/SPARK-5974 and squashes the following commits: c410e38 [Joseph K. Bradley] Added note to LabeledPoint about attributes bcae18b [Joseph K. Bradley] Added import of models for save/load examples in ml guide. Fixed line length for tree.py, feature.py (but not other ML Pyspark files yet). 6d81c3e [Joseph K. Bradley] completed python GBT examples 9903309 [Joseph K. Bradley] Added note to python docs about predict,transform not working within RDD actions,transformations in some cases c7dfad8 [Joseph K. Bradley] Added model save/load to ML guide. Added GBT examples to ML guide
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layout | title | displayTitle |
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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,
which is 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.
Feature values must be nonnegative to represent term frequencies.
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, and output 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)
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()
model.save("myModelPath") val sameModel = NaiveBayesModel.load("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 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; import scala.Tuple2;
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();
model.save("myModelPath"); NaiveBayesModel sameModel = NaiveBayesModel.load("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.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 %}