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| author | Reynold Xin <rxin@apache.org> | 2015-09-17 22:07:42 +0000 |
|---|---|---|
| committer | Reynold Xin <rxin@apache.org> | 2015-09-17 22:07:42 +0000 |
| commit | ee9ffe89d608e7640a2487406b618d27e58026d6 (patch) | |
| tree | 50ec819abb41a9a769d7f64eed1f0ab2084aa6ff /site/docs/1.5.0/mllib-naive-bayes.html | |
| parent | c7104724b279f09486ea62f4a24252e8d06f5c96 (diff) | |
| download | spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.tar.gz spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.tar.bz2 spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.zip | |
delete 1.5.0
Diffstat (limited to 'site/docs/1.5.0/mllib-naive-bayes.html')
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diff --git a/site/docs/1.5.0/mllib-naive-bayes.html b/site/docs/1.5.0/mllib-naive-bayes.html deleted file mode 100644 index f849e34be..000000000 --- a/site/docs/1.5.0/mllib-naive-bayes.html +++ /dev/null @@ -1,306 +0,0 @@ -<!DOCTYPE html> -<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> -<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> -<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> -<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> - <head> - <meta charset="utf-8"> - <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> - <title>Naive Bayes - MLlib - Spark 1.5.0 Documentation</title> - - - - - <link rel="stylesheet" href="css/bootstrap.min.css"> - <style> - body { - padding-top: 60px; - padding-bottom: 40px; - } - </style> - <meta name="viewport" content="width=device-width"> - <link rel="stylesheet" href="css/bootstrap-responsive.min.css"> - <link rel="stylesheet" href="css/main.css"> - - <script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script> - - <link rel="stylesheet" href="css/pygments-default.css"> - - - <!-- Google analytics script --> - <script type="text/javascript"> - var _gaq = _gaq || []; - _gaq.push(['_setAccount', 'UA-32518208-2']); - _gaq.push(['_trackPageview']); - - (function() { - var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; - ga.src = ('https:' == document.location.protocol ? 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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>MLlib 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> - - <div class="highlight"><pre><code class="language-scala" data-lang="scala"><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">"myModelPath"</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">"myModelPath"</span><span class="o">)</span></code></pre></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> - - <div class="highlight"><pre><code class="language-java" data-lang="java"><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.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.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.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="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="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="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">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"myModelPath"</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">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"myModelPath"</span><span class="o">);</span></code></pre></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> - - <div class="highlight"><pre><code class="language-python" data-lang="python"><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">"myModelPath"</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">"myModelPath"</span><span class="p">)</span></code></pre></div> - - </div> -</div> - - - </div> <!-- /container --> - - <script src="js/vendor/jquery-1.8.0.min.js"></script> - <script src="js/vendor/bootstrap.min.js"></script> - <script src="js/vendor/anchor.min.js"></script> - <script src="js/main.js"></script> - - <!-- MathJax Section --> - <script type="text/x-mathjax-config"> - MathJax.Hub.Config({ - TeX: { equationNumbers: { autoNumber: "AMS" } } - }); - </script> - <script> - // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS. - 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