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<h1 class="title">Naive Bayes - spark.mllib</h1>
<p><a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier">Naive Bayes</a> 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.</p>
<p><code>spark.mllib</code> supports <a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes">multinomial naive
Bayes</a>
and <a href="http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html">Bernoulli naive Bayes</a>.
These models are typically used for <a href="http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html">document classification</a>.
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.
<a href="http://en.wikipedia.org/wiki/Lidstone_smoothing">Additive smoothing</a> 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.</p>
<h2 id="examples">Examples</h2>
<div class="codetabs">
<div data-lang="scala">
<p><a href="api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$">NaiveBayes</a> implements
multinomial naive Bayes. It takes an RDD of
<a href="api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint">LabeledPoint</a> and an optional
smoothing parameter <code>lambda</code> as input, an optional model type parameter (default is “multinomial”), and outputs a
<a href="api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel">NaiveBayesModel</a>, which
can be used for evaluation and prediction.</p>
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes"><code>NaiveBayes</code> Scala docs</a> and <a href="api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel"><code>NaiveBayesModel</code> Scala docs</a> for details on the API.</p>
<div class="highlight"><pre><span class="k">import</span> <span class="nn">org.apache.spark.mllib.classification.</span><span class="o">{</span><span class="nc">NaiveBayes</span><span class="o">,</span> <span class="nc">NaiveBayesModel</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span>
<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"data/mllib/sample_naive_bayes_data.txt"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">parsedData</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">line</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">parts</span> <span class="k">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">','</span><span class="o">)</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="n">parts</span><span class="o">(</span><span class="mi">0</span><span class="o">).</span><span class="n">toDouble</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="n">parts</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="n">split</span><span class="o">(</span><span class="sc">' '</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">toDouble</span><span class="o">)))</span>
<span class="o">}</span>
<span class="c1">// Split data into training (60%) and test (40%).</span>
<span class="k">val</span> <span class="n">splits</span> <span class="k">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.6</span><span class="o">,</span> <span class="mf">0.4</span><span class="o">),</span> <span class="n">seed</span> <span class="k">=</span> <span class="mi">11L</span><span class="o">)</span>
<span class="k">val</span> <span class="n">training</span> <span class="k">=</span> <span class="n">splits</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span>
<span class="k">val</span> <span class="n">test</span> <span class="k">=</span> <span class="n">splits</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="nc">NaiveBayes</span><span class="o">.</span><span class="n">train</span><span class="o">(</span><span class="n">training</span><span class="o">,</span> <span class="n">lambda</span> <span class="k">=</span> <span class="mf">1.0</span><span class="o">,</span> <span class="n">modelType</span> <span class="k">=</span> <span class="s">"multinomial"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">predictionAndLabel</span> <span class="k">=</span> <span class="n">test</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">p</span> <span class="k">=></span> <span class="o">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="n">p</span><span class="o">.</span><span class="n">features</span><span class="o">),</span> <span class="n">p</span><span class="o">.</span><span class="n">label</span><span class="o">))</span>
<span class="k">val</span> <span class="n">accuracy</span> <span class="k">=</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="n">predictionAndLabel</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">x</span> <span class="k">=></span> <span class="n">x</span><span class="o">.</span><span class="n">_1</span> <span class="o">==</span> <span class="n">x</span><span class="o">.</span><span class="n">_2</span><span class="o">).</span><span class="n">count</span><span class="o">()</span> <span class="o">/</span> <span class="n">test</span><span class="o">.</span><span class="n">count</span><span class="o">()</span>
<span class="c1">// Save and load model</span>
<span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"target/tmp/myNaiveBayesModel"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sameModel</span> <span class="k">=</span> <span class="nc">NaiveBayesModel</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"target/tmp/myNaiveBayesModel"</span><span class="o">)</span>
</pre></div>
<div><small>Find full example code at "examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala" in the Spark repo.</small></div>
</div>
<div data-lang="java">
<p><a href="api/java/org/apache/spark/mllib/classification/NaiveBayes.html">NaiveBayes</a> implements
multinomial naive Bayes. It takes a Scala RDD of
<a href="api/java/org/apache/spark/mllib/regression/LabeledPoint.html">LabeledPoint</a> and an
optionally smoothing parameter <code>lambda</code> as input, and output a
<a href="api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html">NaiveBayesModel</a>, which
can be used for evaluation and prediction.</p>
<p>Refer to the <a href="api/java/org/apache/spark/mllib/classification/NaiveBayes.html"><code>NaiveBayes</code> Java docs</a> and <a href="api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html"><code>NaiveBayesModel</code> Java docs</a> for details on the API.</p>
<div class="highlight"><pre><span class="kn">import</span> <span class="nn">scala.Tuple2</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.function.Function</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.function.PairFunction</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaPairRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.classification.NaiveBayes</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.classification.NaiveBayesModel</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span>
<span class="n">String</span> <span class="n">path</span> <span class="o">=</span> <span class="s">"data/mllib/sample_naive_bayes_data.txt"</span><span class="o">;</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">inputData</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="n">path</span><span class="o">).</span><span class="na">toJavaRDD</span><span class="o">();</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">>[]</span> <span class="n">tmp</span> <span class="o">=</span> <span class="n">inputData</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]{</span><span class="mf">0.6</span><span class="o">,</span> <span class="mf">0.4</span><span class="o">},</span> <span class="mi">12345</span><span class="o">);</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">training</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> <span class="c1">// training set</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">test</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span> <span class="c1">// test set</span>
<span class="kd">final</span> <span class="n">NaiveBayesModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">NaiveBayes</span><span class="o">.</span><span class="na">train</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="na">rdd</span><span class="o">(),</span> <span class="mf">1.0</span><span class="o">);</span>
<span class="n">JavaPairRDD</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">></span> <span class="n">predictionAndLabel</span> <span class="o">=</span>
<span class="n">test</span><span class="o">.</span><span class="na">mapToPair</span><span class="o">(</span><span class="k">new</span> <span class="n">PairFunction</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>()</span> <span class="o">{</span>
<span class="nd">@Override</span>
<span class="kd">public</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">></span> <span class="nf">call</span><span class="o">(</span><span class="n">LabeledPoint</span> <span class="n">p</span><span class="o">)</span> <span class="o">{</span>
<span class="k">return</span> <span class="k">new</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>(</span><span class="n">model</span><span class="o">.</span><span class="na">predict</span><span class="o">(</span><span class="n">p</span><span class="o">.</span><span class="na">features</span><span class="o">()),</span> <span class="n">p</span><span class="o">.</span><span class="na">label</span><span class="o">());</span>
<span class="o">}</span>
<span class="o">});</span>
<span class="kt">double</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">predictionAndLabel</span><span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="k">new</span> <span class="n">Function</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>,</span> <span class="n">Boolean</span><span class="o">>()</span> <span class="o">{</span>
<span class="nd">@Override</span>
<span class="kd">public</span> <span class="n">Boolean</span> <span class="nf">call</span><span class="o">(</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">></span> <span class="n">pl</span><span class="o">)</span> <span class="o">{</span>
<span class="k">return</span> <span class="n">pl</span><span class="o">.</span><span class="na">_1</span><span class="o">().</span><span class="na">equals</span><span class="o">(</span><span class="n">pl</span><span class="o">.</span><span class="na">_2</span><span class="o">());</span>
<span class="o">}</span>
<span class="o">}).</span><span class="na">count</span><span class="o">()</span> <span class="o">/</span> <span class="o">(</span><span class="kt">double</span><span class="o">)</span> <span class="n">test</span><span class="o">.</span><span class="na">count</span><span class="o">();</span>
<span class="c1">// Save and load model</span>
<span class="n">model</span><span class="o">.</span><span class="na">save</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"target/tmp/myNaiveBayesModel"</span><span class="o">);</span>
<span class="n">NaiveBayesModel</span> <span class="n">sameModel</span> <span class="o">=</span> <span class="n">NaiveBayesModel</span><span class="o">.</span><span class="na">load</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"target/tmp/myNaiveBayesModel"</span><span class="o">);</span>
</pre></div>
<div><small>Find full example code at "examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java" in the Spark repo.</small></div>
</div>
<div data-lang="python">
<p><a href="api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes">NaiveBayes</a> implements multinomial
naive Bayes. It takes an RDD of
<a href="api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint">LabeledPoint</a> and an optionally
smoothing parameter <code>lambda</code> as input, and output a
<a href="api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel">NaiveBayesModel</a>, which can be
used for evaluation and prediction.</p>
<p>Note that the Python API does not yet support model save/load but will in the future.</p>
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes"><code>NaiveBayes</code> Python docs</a> and <a href="api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel"><code>NaiveBayesModel</code> Python docs</a> for more details on the API.</p>
<div class="highlight"><pre><span class="kn">from</span> <span class="nn">pyspark.mllib.classification</span> <span class="kn">import</span> <span class="n">NaiveBayes</span><span class="p">,</span> <span class="n">NaiveBayesModel</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">Vectors</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LabeledPoint</span>
<span class="k">def</span> <span class="nf">parseLine</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
<span class="n">parts</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">','</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">parts</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">parts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">' '</span><span class="p">)])</span>
<span class="k">return</span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">features</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">'data/mllib/sample_naive_bayes_data.txt'</span><span class="p">)</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">parseLine</span><span class="p">)</span>
<span class="c"># Split data aproximately into training (60%) and test (40%)</span>
<span class="n">training</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c"># Train a naive Bayes model.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">NaiveBayes</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
<span class="c"># Make prediction and test accuracy.</span>
<span class="n">predictionAndLabel</span> <span class="o">=</span> <span class="n">test</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">features</span><span class="p">),</span> <span class="n">p</span><span class="o">.</span><span class="n">label</span><span class="p">))</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="n">predictionAndLabel</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> <span class="n">x</span> <span class="o">==</span> <span class="n">v</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span> <span class="o">/</span> <span class="n">test</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="c"># Save and load model</span>
<span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"target/tmp/myNaiveBayesModel"</span><span class="p">)</span>
<span class="n">sameModel</span> <span class="o">=</span> <span class="n">NaiveBayesModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"target/tmp/myNaiveBayesModel"</span><span class="p">)</span>
</pre></div>
<div><small>Find full example code at "examples/src/main/python/mllib/naive_bayes_example.py" in the Spark repo.</small></div>
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