0cfd2cebde
In the previous version, PIC stores clustering assignments as an `RDD[(Long, Int)]`. This is mapped to `RDD<Tuple2<Object, Object>>` in Java and hence Java users have to cast types manually. We should either create a new method called `javaAssignments` that returns `JavaRDD[(java.lang.Long, java.lang.Int)]` or wrap the result pair in a class. I chose the latter approach in this PR. Now assignments are stored as an `RDD[Assignment]`, where `Assignment` is a class with `id` and `cluster`. Similarly, in FPGrowth, the frequent itemsets are stored as an `RDD[(Array[Item], Long)]`, which is mapped to `RDD<Tuple2<Object, Object>>`. Though we provide a "Java-friendly" method `javaFreqItemsets` that returns `JavaRDD[(Array[Item], java.lang.Long)]`. It doesn't really work because `Array[Item]` is mapped to `Object` in Java. So in this PR I created a class `FreqItemset` to wrap the results. It has `items` and `freq`, as well as a `javaItems` method that returns `List<Item>` in Java. I'm not certain that the names I chose are proper: `Assignment`/`id`/`cluster` and `FreqItemset`/`items`/`freq`. Please let me know if there are better suggestions. CC: jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #4695 from mengxr/SPARK-5900 and squashes the following commits: 865b5ca [Xiangrui Meng] make Assignment serializable cffa96e [Xiangrui Meng] fix test 9c0e590 [Xiangrui Meng] remove unused Tuple2 1b9db3d [Xiangrui Meng] make PIC and FPGrowth Java-friendly
99 lines
3.8 KiB
Markdown
99 lines
3.8 KiB
Markdown
---
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layout: global
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title: Frequent Pattern Mining - MLlib
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displayTitle: <a href="mllib-guide.html">MLlib</a> - Frequent Pattern Mining
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---
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Mining frequent items, itemsets, subsequences, or other substructures is usually among the
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first steps to analyze a large-scale dataset, which has been an active research topic in
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data mining for years.
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We refer users to Wikipedia's [association rule learning](http://en.wikipedia.org/wiki/Association_rule_learning)
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for more information.
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MLlib provides a parallel implementation of FP-growth,
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a popular algorithm to mining frequent itemsets.
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## FP-growth
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The FP-growth algorithm is described in the paper
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[Han et al., Mining frequent patterns without candidate generation](http://dx.doi.org/10.1145/335191.335372),
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where "FP" stands for frequent pattern.
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Given a dataset of transactions, the first step of FP-growth is to calculate item frequencies and identify frequent items.
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Different from [Apriori-like](http://en.wikipedia.org/wiki/Apriori_algorithm) algorithms designed for the same purpose,
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the second step of FP-growth uses a suffix tree (FP-tree) structure to encode transactions without generating candidate sets
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explicitly, which are usually expensive to generate.
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After the second step, the frequent itemsets can be extracted from the FP-tree.
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In MLlib, we implemented a parallel version of FP-growth called PFP,
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as described in [Li et al., PFP: Parallel FP-growth for query recommendation](http://dx.doi.org/10.1145/1454008.1454027).
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PFP distributes the work of growing FP-trees based on the suffices of transactions,
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and hence more scalable than a single-machine implementation.
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We refer users to the papers for more details.
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MLlib's FP-growth implementation takes the following (hyper-)parameters:
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* `minSupport`: the minimum support for an itemset to be identified as frequent.
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For example, if an item appears 3 out of 5 transactions, it has a support of 3/5=0.6.
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* `numPartitions`: the number of partitions used to distribute the work.
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**Examples**
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
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FP-growth algorithm.
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It take a `JavaRDD` of transactions, where each transaction is an `Iterable` of items of a generic type.
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Calling `FPGrowth.run` with transactions returns an
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[`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html)
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that stores the frequent itemsets with their frequencies.
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{% highlight scala %}
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import org.apache.spark.rdd.RDD
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import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
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val transactions: RDD[Array[String]] = ...
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val fpg = new FPGrowth()
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.setMinSupport(0.2)
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.setNumPartitions(10)
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val model = fpg.run(transactions)
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model.freqItemsets.collect().foreach { itemset =>
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println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
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}
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{% endhighlight %}
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</div>
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<div data-lang="java" markdown="1">
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[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
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FP-growth algorithm.
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It take an `RDD` of transactions, where each transaction is an `Array` of items of a generic type.
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Calling `FPGrowth.run` with transactions returns an
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[`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html)
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that stores the frequent itemsets with their frequencies.
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{% highlight java %}
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import java.util.List;
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import com.google.common.base.Joiner;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.mllib.fpm.FPGrowth;
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import org.apache.spark.mllib.fpm.FPGrowthModel;
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JavaRDD<List<String>> transactions = ...
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FPGrowth fpg = new FPGrowth()
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.setMinSupport(0.2)
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.setNumPartitions(10);
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FPGrowthModel<String> model = fpg.run(transactions);
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for (FPGrowth.FreqItemset<String> itemset: model.freqItemsets().toJavaRDD().collect()) {
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System.out.println("[" + Joiner.on(",").join(s.javaItems()) + "], " + s.freq());
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}
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{% endhighlight %}
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</div>
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</div>
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