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spark.fpGrowth fits a FP-growth model on a SparkDataFrame. Users can
spark.freqItemsets to get frequent itemsets, spark.associationRules to get
association rules, predict to make predictions on new data based on generated association
rules, and write.ml/read.ml to save/load fitted models.
For more details, see
FP-growth."><!-- Inform modern browsers that this page supports both dark and light color schemes,
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spark.fpGrowth fits a FP-growth model on a SparkDataFrame. Users can
spark.freqItemsets to get frequent itemsets, spark.associationRules to get
association rules, predict to make predictions on new data based on generated association
rules, and write.ml/read.ml to save/load fitted models.
For more details, see
FP-growth."><!-- 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>FP-growth</h1>
<div class="d-none name"><code>spark.fpGrowth.Rd</code></div>
</div>
<div class="ref-description section level2">
<p>A parallel FP-growth algorithm to mine frequent itemsets.
<code>spark.fpGrowth</code> fits a FP-growth model on a SparkDataFrame. Users can
<code>spark.freqItemsets</code> to get frequent itemsets, <code>spark.associationRules</code> to get
association rules, <code>predict</code> to make predictions on new data based on generated association
rules, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models.
For more details, see
<a href="https://spark.apache.org/docs/latest/mllib-frequent-pattern-mining.html#fp-growth" class="external-link">
FP-growth</a>.</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.fpGrowth</span><span class="op">(</span><span class="va">data</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="fu">spark.freqItemsets</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span>
<span></span>
<span><span class="fu">spark.associationRules</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 'SparkDataFrame'</span></span>
<span><span class="fu">spark.fpGrowth</span><span class="op">(</span></span>
<span> <span class="va">data</span>,</span>
<span> minSupport <span class="op">=</span> <span class="fl">0.3</span>,</span>
<span> minConfidence <span class="op">=</span> <span class="fl">0.8</span>,</span>
<span> itemsCol <span class="op">=</span> <span class="st">"items"</span>,</span>
<span> numPartitions <span class="op">=</span> <span class="cn">NULL</span></span>
<span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S4 method for class 'FPGrowthModel'</span></span>
<span><span class="fu">spark.freqItemsets</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 'FPGrowthModel'</span></span>
<span><span class="fu">spark.associationRules</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 'FPGrowthModel'</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 'FPGrowthModel,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>A SparkDataFrame for training.</p></dd>
<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
<dd><p>additional argument(s) passed to the method.</p></dd>
<dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
<dd><p>a fitted FPGrowth model.</p></dd>
<dt id="arg-minsupport">minSupport<a class="anchor" aria-label="anchor" href="#arg-minsupport"></a></dt>
<dd><p>Minimal support level.</p></dd>
<dt id="arg-minconfidence">minConfidence<a class="anchor" aria-label="anchor" href="#arg-minconfidence"></a></dt>
<dd><p>Minimal confidence level.</p></dd>
<dt id="arg-itemscol">itemsCol<a class="anchor" aria-label="anchor" href="#arg-itemscol"></a></dt>
<dd><p>Features column name.</p></dd>
<dt id="arg-numpartitions">numPartitions<a class="anchor" aria-label="anchor" href="#arg-numpartitions"></a></dt>
<dd><p>Number of partitions used for fitting.</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>logical value indicating whether to overwrite 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.fpGrowth</code> returns a fitted FPGrowth model.</p>
<p>A <code>SparkDataFrame</code> with frequent itemsets.
The <code>SparkDataFrame</code> contains two columns:
<code>items</code> (an array of the same type as the input column)
and <code>freq</code> (frequency of the itemset).</p>
<p>A <code>SparkDataFrame</code> with association rules.
The <code>SparkDataFrame</code> contains five columns:
<code>antecedent</code> (an array of the same type as the input column),
<code>consequent</code> (an array of the same type as the input column),
<code>confidence</code> (confidence for the rule)
<code>lift</code> (lift for the rule)
and <code>support</code> (support for the rule)</p>
<p><code>predict</code> returns a SparkDataFrame containing predicted values.</p>
</div>
<div class="section level2">
<h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
<p>spark.fpGrowth since 2.2.0</p>
<p>spark.freqItemsets(FPGrowthModel) since 2.2.0</p>
<p>spark.associationRules(FPGrowthModel) since 2.2.0</p>
<p>predict(FPGrowthModel) since 2.2.0</p>
<p>write.ml(FPGrowthModel, character) since 2.2.0</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 class="co"># \dontrun{</span></span></span>
<span class="r-in"><span><span class="va">raw_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="read.df.html">read.df</a></span><span class="op">(</span></span></span>
<span class="r-in"><span> <span class="st">"data/mllib/sample_fpgrowth.txt"</span>,</span></span>
<span class="r-in"><span> source <span class="op">=</span> <span class="st">"csv"</span>,</span></span>
<span class="r-in"><span> schema <span class="op">=</span> <span class="fu"><a href="structType.html">structType</a></span><span class="op">(</span><span class="fu"><a href="structField.html">structField</a></span><span class="op">(</span><span class="st">"raw_items"</span>, <span class="st">"string"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="selectExpr.html">selectExpr</a></span><span class="op">(</span><span class="va">raw_data</span>, <span class="st">"split(raw_items, ' ') as items"</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">spark.fpGrowth</span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Show frequent itemsets</span></span></span>
<span class="r-in"><span><span class="va">frequent_itemsets</span> <span class="op">&lt;-</span> <span class="fu">spark.freqItemsets</span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="showDF.html">showDF</a></span><span class="op">(</span><span class="va">frequent_itemsets</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Show association rules</span></span></span>
<span class="r-in"><span><span class="va">association_rules</span> <span class="op">&lt;-</span> <span class="fu">spark.associationRules</span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="showDF.html">showDF</a></span><span class="op">(</span><span class="va">association_rules</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Predict on new data</span></span></span>
<span class="r-in"><span><span class="va">new_itemsets</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>items <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">"t"</span>, <span class="st">"t,s"</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">new_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="selectExpr.html">selectExpr</a></span><span class="op">(</span><span class="fu"><a href="createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">new_itemsets</span><span class="op">)</span>, <span class="st">"split(items, ',') as items"</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">new_data</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 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="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></span>
<span class="r-in"><span><span class="co"># Optional arguments</span></span></span>
<span class="r-in"><span><span class="va">baskets_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="selectExpr.html">selectExpr</a></span><span class="op">(</span><span class="fu"><a href="createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">itemsets</span><span class="op">)</span>, <span class="st">"split(items, ',') as baskets"</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">another_model</span> <span class="op">&lt;-</span> <span class="fu">spark.fpGrowth</span><span class="op">(</span><span class="va">data</span>, minSupport <span class="op">=</span> <span class="fl">0.1</span>, minConfidence <span class="op">=</span> <span class="fl">0.5</span>,</span></span>
<span class="r-in"><span> itemsCol <span class="op">=</span> <span class="st">"baskets"</span>, numPartitions <span class="op">=</span> <span class="fl">10</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>
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