1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
|
---
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),
which is 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.
Feature values must be nonnegative to represent term frequencies.
[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, and output 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)
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 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<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.classification.NaiveBayes-class.html) implements multinomial
naive Bayes. It takes an RDD of
[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally
smoothing parameter `lambda` as input, and output a
[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), 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.
<!-- TODO: Make Python's example consistent with Scala's and Java's. -->
{% 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 %}
</div>
</div>
|
