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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Factorization Machines Regression Model</h1>

       <div class="d-none name"><code>spark.fmRegressor.Rd</code></div>
     </div>

     <div class="ref-description section level2">
     <p><code>spark.fmRegressor</code> fits a factorization regression model against a SparkDataFrame.
 Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make
 predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models.</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.fmRegressor</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 SparkDataFrame,formula</span></span>
 <span><span class="fu">spark.fmRegressor</span><span class="op">(</span></span>
 <span>  <span class="va">data</span>,</span>
 <span>  <span class="va">formula</span>,</span>
 <span>  factorSize <span class="op">=</span> <span class="fl">8</span>,</span>
 <span>  fitLinear <span class="op">=</span> <span class="cn">TRUE</span>,</span>
 <span>  regParam <span class="op">=</span> <span class="fl">0</span>,</span>
 <span>  miniBatchFraction <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>  initStd <span class="op">=</span> <span class="fl">0.01</span>,</span>
 <span>  maxIter <span class="op">=</span> <span class="fl">100</span>,</span>
 <span>  stepSize <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>  tol <span class="op">=</span> <span class="fl">1e-06</span>,</span>
 <span>  solver <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">"adamW"</span>, <span class="st">"gd"</span><span class="op">)</span>,</span>
 <span>  seed <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  stringIndexerOrderType <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">"frequencyDesc"</span>, <span class="st">"frequencyAsc"</span>, <span class="st">"alphabetDesc"</span>,</span>
 <span>    <span class="st">"alphabetAsc"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S4 method for FMRegressionModel</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 FMRegressionModel</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 FMRegressionModel,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>data</dt>
 <dd><p>a <code>SparkDataFrame</code> of observations and labels for model fitting.</p></dd>


 <dt>formula</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>...</dt>
 <dd><p>additional arguments passed to the method.</p></dd>


 <dt>factorSize</dt>
 <dd><p>dimensionality of the factors.</p></dd>


 <dt>fitLinear</dt>
 <dd><p>whether to fit linear term.  # TODO Can we express this with formula?</p></dd>


 <dt>regParam</dt>
 <dd><p>the regularization parameter.</p></dd>


 <dt>miniBatchFraction</dt>
 <dd><p>the mini-batch fraction parameter.</p></dd>


 <dt>initStd</dt>
 <dd><p>the standard deviation of initial coefficients.</p></dd>


 <dt>maxIter</dt>
 <dd><p>maximum iteration number.</p></dd>


 <dt>stepSize</dt>
 <dd><p>stepSize parameter.</p></dd>


 <dt>tol</dt>
 <dd><p>convergence tolerance of iterations.</p></dd>


 <dt>solver</dt>
 <dd><p>solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".</p></dd>


 <dt>seed</dt>
 <dd><p>seed parameter for weights initialization.</p></dd>


 <dt>stringIndexerOrderType</dt>
 <dd><p>how to order categories of a string feature column. This is used to
 decide the base level of a string feature as the last category
 after ordering is dropped when encoding strings. Supported options
 are "frequencyDesc", "frequencyAsc", "alphabetDesc", and
 "alphabetAsc". The default value is "frequencyDesc". When the
 ordering is set to "alphabetDesc", this drops the same category
 as R when encoding strings.</p></dd>


 <dt>object</dt>
 <dd><p>a FM Regression Model model fitted by <code>spark.fmRegressor</code>.</p></dd>


 <dt>newData</dt>
 <dd><p>a SparkDataFrame for testing.</p></dd>


 <dt>path</dt>
 <dd><p>The directory where the model is saved.</p></dd>


 <dt>overwrite</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.fmRegressor</code> returns a fitted Factorization Machines Regression Model.</p>


 <p><code>summary</code> returns summary information of the fitted model, which is a list.</p>


 <p><code>predict</code> returns the predicted values based on an FMRegressionModel.</p>
     </div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>spark.fmRegressor since 3.1.0</p>
 <p>summary(FMRegressionModel) since 3.1.0</p>
 <p>predict(FMRegressionModel) since 3.1.0</p>
 <p>write.ml(FMRegressionModel, character) since 3.1.0</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="read.ml.html">read.ml</a></p></div>
     </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></span>
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu"><a href="read.df.html">read.df</a></span><span class="op">(</span><span class="st">"data/mllib/sample_linear_regression_data.txt"</span>, source <span class="op">=</span> <span class="st">"libsvm"</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># fit Factorization Machines Regression Model</span></span></span>
 <span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">spark.fmRegressor</span><span class="op">(</span></span></span>
 <span class="r-in"><span>  <span class="va">df</span>, <span class="va">label</span> <span class="op">~</span> <span class="va">features</span>,</span></span>
 <span class="r-in"><span>  regParam <span class="op">=</span> <span class="fl">0.01</span>, maxIter <span class="op">=</span> <span class="fl">10</span>, fitLinear <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># get the summary of the model</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">model</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># make predictions</span></span></span>
 <span class="r-in"><span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">df</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># save and load the model</span></span></span>
 <span class="r-in"><span><span class="va">path</span> <span class="op">&lt;-</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 class="va">savedModel</span> <span class="op">&lt;-</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></span>
 </code></pre></div>
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	<img src="" class="logo" alt=""><h1>Factorization Machines Regression Model</h1>

	<div class="d-none name"><code>spark.fmRegressor.Rd</code></div>
	</div>

	<div class="ref-description section level2">
	<p><code>spark.fmRegressor</code> fits a factorization regression model against a SparkDataFrame.
	Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make
	predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models.</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.fmRegressor</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 SparkDataFrame,formula</span></span>
	<span><span class="fu">spark.fmRegressor</span><span class="op">(</span></span>
	<span> <span class="va">data</span>,</span>
	<span> <span class="va">formula</span>,</span>
	<span> factorSize <span class="op">=</span> <span class="fl">8</span>,</span>
	<span> fitLinear <span class="op">=</span> <span class="cn">TRUE</span>,</span>
	<span> regParam <span class="op">=</span> <span class="fl">0</span>,</span>
	<span> miniBatchFraction <span class="op">=</span> <span class="fl">1</span>,</span>
	<span> initStd <span class="op">=</span> <span class="fl">0.01</span>,</span>
	<span> maxIter <span class="op">=</span> <span class="fl">100</span>,</span>
	<span> stepSize <span class="op">=</span> <span class="fl">1</span>,</span>
	<span> tol <span class="op">=</span> <span class="fl">1e-06</span>,</span>
	<span> solver <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">"adamW"</span>, <span class="st">"gd"</span><span class="op">)</span>,</span>
	<span> seed <span class="op">=</span> <span class="cn">NULL</span>,</span>
	<span> stringIndexerOrderType <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">"frequencyDesc"</span>, <span class="st">"frequencyAsc"</span>, <span class="st">"alphabetDesc"</span>,</span>
	<span> <span class="st">"alphabetAsc"</span><span class="op">)</span></span>
	<span><span class="op">)</span></span>
	<span></span>
	<span><span class="co"># S4 method for FMRegressionModel</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 FMRegressionModel</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 FMRegressionModel,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>data</dt>
	<dd><p>a <code>SparkDataFrame</code> of observations and labels for model fitting.</p></dd>


	<dt>formula</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>...</dt>
	<dd><p>additional arguments passed to the method.</p></dd>


	<dt>factorSize</dt>
	<dd><p>dimensionality of the factors.</p></dd>


	<dt>fitLinear</dt>
	<dd><p>whether to fit linear term. # TODO Can we express this with formula?</p></dd>


	<dt>regParam</dt>
	<dd><p>the regularization parameter.</p></dd>


	<dt>miniBatchFraction</dt>
	<dd><p>the mini-batch fraction parameter.</p></dd>


	<dt>initStd</dt>
	<dd><p>the standard deviation of initial coefficients.</p></dd>


	<dt>maxIter</dt>
	<dd><p>maximum iteration number.</p></dd>


	<dt>stepSize</dt>
	<dd><p>stepSize parameter.</p></dd>


	<dt>tol</dt>
	<dd><p>convergence tolerance of iterations.</p></dd>


	<dt>solver</dt>
	<dd><p>solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".</p></dd>


	<dt>seed</dt>
	<dd><p>seed parameter for weights initialization.</p></dd>


	<dt>stringIndexerOrderType</dt>
	<dd><p>how to order categories of a string feature column. This is used to
	decide the base level of a string feature as the last category
	after ordering is dropped when encoding strings. Supported options
	are "frequencyDesc", "frequencyAsc", "alphabetDesc", and
	"alphabetAsc". The default value is "frequencyDesc". When the
	ordering is set to "alphabetDesc", this drops the same category
	as R when encoding strings.</p></dd>


	<dt>object</dt>
	<dd><p>a FM Regression Model model fitted by <code>spark.fmRegressor</code>.</p></dd>


	<dt>newData</dt>
	<dd><p>a SparkDataFrame for testing.</p></dd>


	<dt>path</dt>
	<dd><p>The directory where the model is saved.</p></dd>


	<dt>overwrite</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.fmRegressor</code> returns a fitted Factorization Machines Regression Model.</p>


	<p><code>summary</code> returns summary information of the fitted model, which is a list.</p>


	<p><code>predict</code> returns the predicted values based on an FMRegressionModel.</p>
	</div>
	<div class="section level2">
	<h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
	<p>spark.fmRegressor since 3.1.0</p>
	<p>summary(FMRegressionModel) since 3.1.0</p>
	<p>predict(FMRegressionModel) since 3.1.0</p>
	<p>write.ml(FMRegressionModel, character) since 3.1.0</p>
	</div>
	<div class="section level2">
	<h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
	<div class="dont-index"><p><a href="read.ml.html">read.ml</a></p></div>
	</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></span>
	<span class="r-in"><span><span class="va">df</span> <span class="op"><-</span> <span class="fu"><a href="read.df.html">read.df</a></span><span class="op">(</span><span class="st">"data/mllib/sample_linear_regression_data.txt"</span>, source <span class="op">=</span> <span class="st">"libsvm"</span><span class="op">)</span></span></span>
	<span class="r-in"><span></span></span>
	<span class="r-in"><span><span class="co"># fit Factorization Machines Regression Model</span></span></span>
	<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu">spark.fmRegressor</span><span class="op">(</span></span></span>
	<span class="r-in"><span> <span class="va">df</span>, <span class="va">label</span> <span class="op">~</span> <span class="va">features</span>,</span></span>
	<span class="r-in"><span> regParam <span class="op">=</span> <span class="fl">0.01</span>, maxIter <span class="op">=</span> <span class="fl">10</span>, fitLinear <span class="op">=</span> <span class="cn">TRUE</span></span></span>
	<span class="r-in"><span><span class="op">)</span></span></span>
	<span class="r-in"><span></span></span>
	<span class="r-in"><span><span class="co"># get the summary of the model</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">model</span><span class="op">)</span></span></span>
	<span class="r-in"><span></span></span>
	<span class="r-in"><span><span class="co"># make predictions</span></span></span>
	<span class="r-in"><span><span class="va">predictions</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">df</span><span class="op">)</span></span></span>
	<span class="r-in"><span></span></span>
	<span class="r-in"><span><span class="co"># save and load the model</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 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></span>
	</code></pre></div>
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