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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-isotonic-regression.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-isotonic-regression.html')
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diff --git a/site/docs/1.5.0/mllib-isotonic-regression.html b/site/docs/1.5.0/mllib-isotonic-regression.html deleted file mode 100644 index 85aa08f58..000000000 --- a/site/docs/1.5.0/mllib-isotonic-regression.html +++ /dev/null @@ -1,357 +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>Isotonic regression - 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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Formally isotonic regression is a problem where -given a finite set of real numbers <code>$Y = {y_1, y_2, ..., y_n}$</code> representing observed responses -and <code>$X = {x_1, x_2, ..., x_n}$</code> the unknown response values to be fitted -finding a function that minimises</p> - -<p><code>\begin{equation} - f(x) = \sum_{i=1}^n w_i (y_i - x_i)^2 -\end{equation}</code></p> - -<p>with respect to complete order subject to -<code>$x_1\le x_2\le ...\le x_n$</code> where <code>$w_i$</code> are positive weights. -The resulting function is called isotonic regression and it is unique. -It can be viewed as least squares problem under order restriction. -Essentially isotonic regression is a -<a href="http://en.wikipedia.org/wiki/Monotonic_function">monotonic function</a> -best fitting the original data points.</p> - -<p>MLlib supports a -<a href="http://doi.org/10.1198/TECH.2010.10111">pool adjacent violators algorithm</a> -which uses an approach to -<a href="http://doi.org/10.1007/978-3-642-99789-1_10">parallelizing isotonic regression</a>. -The training input is a RDD of tuples of three double values that represent -label, feature and weight in this order. Additionally IsotonicRegression algorithm has one -optional parameter called $isotonic$ defaulting to true. -This argument specifies if the isotonic regression is -isotonic (monotonically increasing) or antitonic (monotonically decreasing).</p> - -<p>Training returns an IsotonicRegressionModel that can be used to predict -labels for both known and unknown features. The result of isotonic regression -is treated as piecewise linear function. The rules for prediction therefore are:</p> - -<ul> - <li>If the prediction input exactly matches a training feature -then associated prediction is returned. In case there are multiple predictions with the same -feature then one of them is returned. Which one is undefined -(same as java.util.Arrays.binarySearch).</li> - <li>If the prediction input is lower or higher than all training features -then prediction with lowest or highest feature is returned respectively. -In case there are multiple predictions with the same feature -then the lowest or highest is returned respectively.</li> - <li>If the prediction input falls between two training features then prediction is treated -as piecewise linear function and interpolated value is calculated from the -predictions of the two closest features. In case there are multiple values -with the same feature then the same rules as in previous point are used.</li> -</ul> - -<h3 id="examples">Examples</h3> - -<div class="codetabs"> -<div data-lang="scala"> - <p>Data are read from a file where each line has a format label,feature -i.e. 4710.28,500.00. The data are split to training and testing set. -Model is created using the training set and a mean squared error is calculated from the predicted -labels and real labels in the test set.</p> - - <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.</span><span class="o">{</span><span class="nc">IsotonicRegression</span><span class="o">,</span> <span class="nc">IsotonicRegressionModel</span><span class="o">}</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_isotonic_regression_data.txt"</span><span class="o">)</span> - -<span class="c1">// Create label, feature, weight tuples from input data with weight set to default value 1.0.</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="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="n">parts</span><span class="o">(</span><span class="mi">0</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="mf">1.0</span><span class="o">)</span> -<span class="o">}</span> - -<span class="c1">// Split data into training (60%) and test (40%) sets.</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="c1">// Create isotonic regression model from training data.</span> -<span class="c1">// Isotonic parameter defaults to true so it is only shown for demonstration</span> -<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">IsotonicRegression</span><span class="o">().</span><span class="n">setIsotonic</span><span class="o">(</span><span class="kc">true</span><span class="o">).</span><span class="n">run</span><span class="o">(</span><span class="n">training</span><span class="o">)</span> - -<span class="c1">// Create tuples of predicted and real labels.</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">point</span> <span class="k">=></span> - <span class="k">val</span> <span class="n">predictedLabel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="n">_2</span><span class="o">)</span> - <span class="o">(</span><span class="n">predictedLabel</span><span class="o">,</span> <span class="n">point</span><span class="o">.</span><span class="n">_1</span><span class="o">)</span> -<span class="o">}</span> - -<span class="c1">// Calculate mean squared error between predicted and real labels.</span> -<span class="k">val</span> <span class="n">meanSquaredError</span> <span class="k">=</span> <span class="n">predictionAndLabel</span><span class="o">.</span><span class="n">map</span><span class="o">{</span><span class="k">case</span><span class="o">(</span><span class="n">p</span><span class="o">,</span> <span class="n">l</span><span class="o">)</span> <span class="k">=></span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="o">((</span><span class="n">p</span> <span class="o">-</span> <span class="n">l</span><span class="o">),</span> <span class="mi">2</span><span class="o">)}.</span><span class="n">mean</span><span class="o">()</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="n">meanSquaredError</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">IsotonicRegressionModel</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>Data are read from a file where each line has a format label,feature -i.e. 4710.28,500.00. The data are split to training and testing set. -Model is created using the training set and a mean squared error is calculated from the predicted -labels and real labels in the test set.</p> - - <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaDoubleRDD</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.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.regression.IsotonicRegressionModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">scala.Tuple2</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">scala.Tuple3</span><span class="o">;</span> - -<span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="s">"data/mllib/sample_isotonic_regression_data.txt"</span><span class="o">);</span> - -<span class="c1">// Create label, feature, weight tuples from input data with weight set to default value 1.0.</span> -<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Tuple3</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">Double</span><span class="o">>></span> <span class="n">parsedData</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="na">map</span><span class="o">(</span> - <span class="k">new</span> <span class="n">Function</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Tuple3</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">Double</span><span class="o">>>()</span> <span class="o">{</span> - <span class="kd">public</span> <span class="n">Tuple3</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">Double</span><span class="o">></span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">line</span><span class="o">)</span> <span class="o">{</span> - <span class="n">String</span><span class="o">[]</span> <span class="n">parts</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">","</span><span class="o">);</span> - <span class="k">return</span> <span class="k">new</span> <span class="n">Tuple3</span><span class="o"><>(</span><span class="k">new</span> <span class="nf">Double</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="k">new</span> <span class="nf">Double</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="mf">1.0</span><span class="o">);</span> - <span class="o">}</span> - <span class="o">}</span> -<span class="o">);</span> - -<span class="c1">// Split data into training (60%) and test (40%) sets.</span> -<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Tuple3</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">Double</span><span class="o">>>[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">parsedData</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="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">11L</span><span class="o">);</span> -<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Tuple3</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">Double</span><span class="o">>></span> <span class="n">training</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> -<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Tuple3</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">Double</span><span class="o">>></span> <span class="n">test</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span> - -<span class="c1">// Create isotonic regression model from training data.</span> -<span class="c1">// Isotonic parameter defaults to true so it is only shown for demonstration</span> -<span class="n">IsotonicRegressionModel</span> <span class="n">model</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">IsotonicRegression</span><span class="o">().</span><span class="na">setIsotonic</span><span class="o">(</span><span class="kc">true</span><span class="o">).</span><span class="na">run</span><span class="o">(</span><span class="n">training</span><span class="o">);</span> - -<span class="c1">// Create tuples of predicted and real labels.</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">Tuple3</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">Double</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">Tuple3</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">Double</span><span class="o">></span> <span class="n">point</span><span class="o">)</span> <span class="o">{</span> - <span class="n">Double</span> <span class="n">predictedLabel</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">point</span><span class="o">.</span><span class="na">_2</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">predictedLabel</span><span class="o">,</span> <span class="n">point</span><span class="o">.</span><span class="na">_1</span><span class="o">());</span> - <span class="o">}</span> - <span class="o">}</span> -<span class="o">);</span> - -<span class="c1">// Calculate mean squared error between predicted and real labels.</span> -<span class="n">Double</span> <span class="n">meanSquaredError</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaDoubleRDD</span><span class="o">(</span><span class="n">predictionAndLabel</span><span class="o">.</span><span class="na">map</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">Object</span><span class="o">>()</span> <span class="o">{</span> - <span class="nd">@Override</span> <span class="kd">public</span> <span class="n">Object</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">Math</span><span class="o">.</span><span class="na">pow</span><span class="o">(</span><span class="n">pl</span><span class="o">.</span><span class="na">_1</span><span class="o">()</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="mi">2</span><span class="o">);</span> - <span class="o">}</span> - <span class="o">}</span> -<span class="o">).</span><span class="na">rdd</span><span class="o">()).</span><span class="na">mean</span><span class="o">();</span> - -<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="n">meanSquaredError</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">IsotonicRegressionModel</span> <span class="n">sameModel</span> <span class="o">=</span> <span class="n">IsotonicRegressionModel</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>Data are read from a file where each line has a format label,feature -i.e. 4710.28,500.00. The data are split to training and testing set. -Model is created using the training set and a mean squared error is calculated from the predicted -labels and real labels in the test set.</p> - - <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">math</span> -<span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">IsotonicRegression</span><span class="p">,</span> <span class="n">IsotonicRegressionModel</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_isotonic_regression_data.txt"</span><span class="p">)</span> - -<span class="c"># Create label, feature, weight tuples from input data with weight set to default value 1.0.</span> -<span class="n">parsedData</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="nb">tuple</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">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="o">+</span> <span class="p">(</span><span class="mf">1.0</span><span class="p">,))</span> - -<span class="c"># Split data into training (60%) and test (40%) sets.</span> -<span class="n">training</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">parsedData</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="mi">11</span><span class="p">)</span> - -<span class="c"># Create isotonic regression model from training data.</span> -<span class="c"># Isotonic parameter defaults to true so it is only shown for demonstration</span> -<span class="n">model</span> <span class="o">=</span> <span class="n">IsotonicRegression</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="c"># Create tuples of predicted and real labels.</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="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> - -<span class="c"># Calculate mean squared error between predicted and real labels.</span> -<span class="n">meanSquaredError</span> <span class="o">=</span> <span class="n">predictionAndLabel</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">pl</span><span class="p">:</span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="p">((</span><span class="n">pl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">pl</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> -<span class="k">print</span><span class="p">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">meanSquaredError</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">IsotonicRegressionModel</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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