33edb2b79e
jira: https://issues.apache.org/jira/browse/SPARK-8043
I found some issues during testing the save/load examples in markdown Documents, as a part of 1.4 QA plan
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #6584 from hhbyyh/naiveDocExample and squashes the following commits:
a01a206 [Yuhao Yang] fix for Gaussian mixture
2fb8b96 [Yuhao Yang] update NaiveBayes and SVM examples in doc
(cherry picked from commit 43adbd5611
)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
147 lines
6.3 KiB
Markdown
147 lines
6.3 KiB
Markdown
---
|
|
layout: global
|
|
title: Naive Bayes - MLlib
|
|
displayTitle: <a href="mllib-guide.html">MLlib</a> - Naive Bayes
|
|
---
|
|
|
|
[Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) 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](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes)
|
|
and [Bernoulli naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html).
|
|
These models are typically used for [document classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html).
|
|
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](http://en.wikipedia.org/wiki/Lidstone_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
|
|
|
|
<div class="codetabs">
|
|
<div data-lang="scala" markdown="1">
|
|
|
|
[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
|
|
multinomial naive Bayes. It takes an RDD of
|
|
[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional
|
|
smoothing parameter `lambda` as input, an optional model type parameter (default is "multinomial"), and outputs a
|
|
[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.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 %}
|
|
</div>
|
|
|
|
<div data-lang="java" markdown="1">
|
|
|
|
[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements
|
|
multinomial naive Bayes. It takes a Scala RDD of
|
|
[LabeledPoint](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) and an
|
|
optionally smoothing parameter `lambda` as input, and output a
|
|
[NaiveBayesModel](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html), 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<LabeledPoint> training = ... // training set
|
|
JavaRDD<LabeledPoint> 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 %}
|
|
</div>
|
|
|
|
<div data-lang="python" markdown="1">
|
|
|
|
[NaiveBayes](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes) implements multinomial
|
|
naive Bayes. It takes an RDD of
|
|
[LabeledPoint](api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint) and an optionally
|
|
smoothing parameter `lambda` as input, and output a
|
|
[NaiveBayesModel](api/python/pyspark.mllib.html#pyspark.mllib.classification.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
|
|
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()
|
|
{% endhighlight %}
|
|
|
|
</div>
|
|
</div>
|