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| Currently only supports binary classification model with linear kernel. |
| Users can print, make predictions on the produced model and save the model to the input path."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> |
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| |
| <h1>Linear SVM Model</h1> |
| |
| <div class="d-none name"><code>spark.svmLinear.Rd</code></div> |
| </div> |
| |
| <div class="ref-description section level2"> |
| <p>Fits a linear SVM model against a SparkDataFrame, similar to svm in e1071 package. |
| Currently only supports binary classification model with linear kernel. |
| Users can print, make predictions on the produced model and save the model to the input path.</p> |
| </div> |
| |
| <div class="section level2"> |
| <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2> |
| <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">spark.svmLinear</span><span class="op">(</span><span class="va">data</span>, <span class="va">formula</span>, <span class="va">...</span><span class="op">)</span></span> |
| <span></span> |
| <span><span class="co"># S4 method for class 'SparkDataFrame,formula'</span></span> |
| <span><span class="fu">spark.svmLinear</span><span class="op">(</span></span> |
| <span> <span class="va">data</span>,</span> |
| <span> <span class="va">formula</span>,</span> |
| <span> regParam <span class="op">=</span> <span class="fl">0</span>,</span> |
| <span> maxIter <span class="op">=</span> <span class="fl">100</span>,</span> |
| <span> tol <span class="op">=</span> <span class="fl">1e-06</span>,</span> |
| <span> standardization <span class="op">=</span> <span class="cn">TRUE</span>,</span> |
| <span> threshold <span class="op">=</span> <span class="fl">0</span>,</span> |
| <span> weightCol <span class="op">=</span> <span class="cn">NULL</span>,</span> |
| <span> aggregationDepth <span class="op">=</span> <span class="fl">2</span>,</span> |
| <span> handleInvalid <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"error"</span>, <span class="st">"keep"</span>, <span class="st">"skip"</span><span class="op">)</span></span> |
| <span><span class="op">)</span></span> |
| <span></span> |
| <span><span class="co"># S4 method for class 'LinearSVCModel'</span></span> |
| <span><span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">newData</span><span class="op">)</span></span> |
| <span></span> |
| <span><span class="co"># S4 method for class 'LinearSVCModel'</span></span> |
| <span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span> |
| <span></span> |
| <span><span class="co"># S4 method for class 'LinearSVCModel,character'</span></span> |
| <span><span class="fu"><a href="write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">path</span>, overwrite <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> |
| </div> |
| |
| <div class="section level2"> |
| <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2> |
| |
| |
| <dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt> |
| <dd><p>SparkDataFrame for training.</p></dd> |
| |
| |
| <dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt> |
| <dd><p>A symbolic description of the model to be fitted. Currently only a few formula |
| operators are supported, including '~', '.', ':', '+', '-', '*', and '^'.</p></dd> |
| |
| |
| <dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt> |
| <dd><p>additional arguments passed to the method.</p></dd> |
| |
| |
| <dt id="arg-regparam">regParam<a class="anchor" aria-label="anchor" href="#arg-regparam"></a></dt> |
| <dd><p>The regularization parameter. Only supports L2 regularization currently.</p></dd> |
| |
| |
| <dt id="arg-maxiter">maxIter<a class="anchor" aria-label="anchor" href="#arg-maxiter"></a></dt> |
| <dd><p>Maximum iteration number.</p></dd> |
| |
| |
| <dt id="arg-tol">tol<a class="anchor" aria-label="anchor" href="#arg-tol"></a></dt> |
| <dd><p>Convergence tolerance of iterations.</p></dd> |
| |
| |
| <dt id="arg-standardization">standardization<a class="anchor" aria-label="anchor" href="#arg-standardization"></a></dt> |
| <dd><p>Whether to standardize the training features before fitting the model. |
| The coefficients of models will be always returned on the original scale, |
| so it will be transparent for users. Note that with/without |
| standardization, the models should be always converged to the same |
| solution when no regularization is applied.</p></dd> |
| |
| |
| <dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt> |
| <dd><p>The threshold in binary classification applied to the linear model prediction. |
| This threshold can be any real number, where Inf will make all predictions 0.0 |
| and -Inf will make all predictions 1.0.</p></dd> |
| |
| |
| <dt id="arg-weightcol">weightCol<a class="anchor" aria-label="anchor" href="#arg-weightcol"></a></dt> |
| <dd><p>The weight column name.</p></dd> |
| |
| |
| <dt id="arg-aggregationdepth">aggregationDepth<a class="anchor" aria-label="anchor" href="#arg-aggregationdepth"></a></dt> |
| <dd><p>The depth for treeAggregate (greater than or equal to 2). If the |
| dimensions of features or the number of partitions are large, this param |
| could be adjusted to a larger size. |
| This is an expert parameter. Default value should be good for most cases.</p></dd> |
| |
| |
| <dt id="arg-handleinvalid">handleInvalid<a class="anchor" aria-label="anchor" href="#arg-handleinvalid"></a></dt> |
| <dd><p>How to handle invalid data (unseen labels or NULL values) in features and |
| label column of string type. |
| Supported options: "skip" (filter out rows with invalid data), |
| "error" (throw an error), "keep" (put invalid data in |
| a special additional bucket, at index numLabels). Default |
| is "error".</p></dd> |
| |
| |
| <dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt> |
| <dd><p>a LinearSVCModel fitted by <code>spark.svmLinear</code>.</p></dd> |
| |
| |
| <dt id="arg-newdata">newData<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt> |
| <dd><p>a SparkDataFrame for testing.</p></dd> |
| |
| |
| <dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt> |
| <dd><p>The directory where the model is saved.</p></dd> |
| |
| |
| <dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt> |
| <dd><p>Overwrites or not if the output path already exists. Default is FALSE |
| which means throw exception if the output path exists.</p></dd> |
| |
| </dl></div> |
| <div class="section level2"> |
| <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2> |
| <p><code>spark.svmLinear</code> returns a fitted linear SVM model.</p> |
| <p><code>predict</code> returns the predicted values based on a LinearSVCModel.</p> |
| <p><code>summary</code> returns summary information of the fitted model, which is a list. |
| The list includes <code>coefficients</code> (coefficients of the fitted model), |
| <code>numClasses</code> (number of classes), <code>numFeatures</code> (number of features).</p> |
| </div> |
| <div class="section level2"> |
| <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2> |
| <p>spark.svmLinear since 2.2.0</p> |
| <p>predict(LinearSVCModel) since 2.2.0</p> |
| <p>summary(LinearSVCModel) since 2.2.0</p> |
| <p>write.ml(LogisticRegression, character) since 2.2.0</p> |
| </div> |
| |
| <div class="section level2"> |
| <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2> |
| <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span> |
| <span class="r-in"><span><span class="fu"><a href="sparkR.session.html">sparkR.session</a></span><span class="op">(</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="va">t</span> <span class="op"><-</span> <span class="fu"><a href="as.data.frame.html">as.data.frame</a></span><span class="op">(</span><span class="va">Titanic</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="va">training</span> <span class="op"><-</span> <span class="fu"><a href="createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu">spark.svmLinear</span><span class="op">(</span><span class="va">training</span>, <span class="va">Survived</span> <span class="op">~</span> <span class="va">.</span>, regParam <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="va">summary</span> <span class="op"><-</span> <span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span> |
| <span class="r-in"><span></span></span> |
| <span class="r-in"><span><span class="co"># fitted values on training data</span></span></span> |
| <span class="r-in"><span><span class="va">fitted</span> <span class="op"><-</span> <span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">training</span><span class="op">)</span></span></span> |
| <span class="r-in"><span></span></span> |
| <span class="r-in"><span><span class="co"># save fitted model to input path</span></span></span> |
| <span class="r-in"><span><span class="va">path</span> <span class="op"><-</span> <span class="st">"path/to/model"</span></span></span> |
| <span class="r-in"><span><span class="fu"><a href="write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">path</span><span class="op">)</span></span></span> |
| <span class="r-in"><span></span></span> |
| <span class="r-in"><span><span class="co"># can also read back the saved model and predict</span></span></span> |
| <span class="r-in"><span><span class="co"># Note that summary deos not work on loaded model</span></span></span> |
| <span class="r-in"><span><span class="va">savedModel</span> <span class="op"><-</span> <span class="fu"><a href="read.ml.html">read.ml</a></span><span class="op">(</span><span class="va">path</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">savedModel</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span> |
| </code></pre></div> |
| </div> |
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