[SPARK-5939][MLLib] make FPGrowth example app take parameters

data/mllib/sample_fpgrowth.txt

@@ -0,0 +1,6 @@
+r z h k p
+z y x w v u t s
+s x o n r
+x z y m t s q e
+z
+x z y r q t p

examples/src/main/java/org/apache/spark/examples/mllib/JavaFPGrowthExample.java

@@ -25,32 +25,49 @@
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
 import org.apache.spark.mllib.fpm.FPGrowth;
 import org.apache.spark.mllib.fpm.FPGrowthModel;
 
 /**
  * Java example for mining frequent itemsets using FP-growth.
+ * Example usage:  ./bin/run-example org.apache.spark.examples.mllib.JavaFPGrowthExample
+ * ./data/mllib/sample_fpgrowth.txt
  */
 public class JavaFPGrowthExample {
 
   public static void main(String[] args) {
+    String inputFile;
+    double minSupport = 0.3;
+    int numPartition = -1;
+    if (args.length < 1) {
+      System.err.println(
+              "Usage: JavaKMeans <input_file> [minSupport] [numPartition]");
+      System.exit(1);
+    }
+    inputFile = args[0];
+    if (args.length >= 2) {
+      minSupport = Double.parseDouble(args[1]);
+    }
+    if (args.length >= 3) {
+      numPartition = Integer.parseInt(args[2]);
+    }
+
     SparkConf sparkConf = new SparkConf().setAppName("JavaFPGrowthExample");
     JavaSparkContext sc = new JavaSparkContext(sparkConf);
 
+    JavaRDD<ArrayList<String>> transactions = sc.textFile(inputFile).map(
+      new Function<String, ArrayList<String>>() {
+      @Override
+      public ArrayList<String> call(String s) {
+        return Lists.newArrayList(s.split(" "));
+      }
+    });
 
-    // TODO: Read a user-specified input file.
-    @SuppressWarnings("unchecked")
-    JavaRDD<ArrayList<String>> transactions = sc.parallelize(Lists.newArrayList(
-      Lists.newArrayList("r z h k p".split(" ")),
-      Lists.newArrayList("z y x w v u t s".split(" ")),
-      Lists.newArrayList("s x o n r".split(" ")),
-      Lists.newArrayList("x z y m t s q e".split(" ")),
-      Lists.newArrayList("z".split(" ")),
-      Lists.newArrayList("x z y r q t p".split(" "))), 2);
-
-    FPGrowth fpg = new FPGrowth()
-      .setMinSupport(0.3);
-    FPGrowthModel<String> model = fpg.run(transactions);
+    FPGrowthModel<String> model = new FPGrowth()
+      .setMinSupport(minSupport)
+      .setNumPartitions(numPartition)
+      .run(transactions);
 
     for (FPGrowth.FreqItemset<String> s: model.freqItemsets().toJavaRDD().collect()) {
       System.out.println("[" + Joiner.on(",").join(s.javaItems()) + "], " + s.freq());

examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala

@@ -18,29 +18,58 @@
 package org.apache.spark.examples.mllib
 
 import org.apache.spark.mllib.fpm.FPGrowth
-import org.apache.spark.{SparkContext, SparkConf}
+import org.apache.spark.{SparkConf, SparkContext}
+import scopt.OptionParser
 
 /**
  * Example for mining frequent itemsets using FP-growth.
+ * Example usage: ./bin/run-example org.apache.spark.examples.mllib.FPGrowthExample
+ * --minSupport 0.8 --numPartition 2 ./data/mllib/sample_fpgrowth.txt
  */
 object FPGrowthExample {
 
+  case class Params(
+    input: String = null,
+    minSupport: Double = 0.3,
+    numPartition: Int = -1) extends AbstractParams[Params]
+
   def main(args: Array[String]) {
-    val conf = new SparkConf().setAppName("FPGrowthExample")
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("FPGrowth") {
+      head("FPGrowth: an example FP-growth app.")
+      opt[Double]("minSupport")
+        .text(s"minimal support level, default: ${defaultParams.minSupport}")
+        .action((x, c) => c.copy(minSupport = x))
+      opt[Int]("numPartition")
+        .text(s"number of partition, default: ${defaultParams.numPartition}")
+        .action((x, c) => c.copy(numPartition = x))
+      arg[String]("<input>")
+        .text("input paths to input data set")
+        .required()
+        .action((x, c) => c.copy(input = x))
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    }.getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"FPGrowthExample with $params")
     val sc = new SparkContext(conf)
+    val transactions = sc.textFile(params.input).map(_.split(" ")).cache()
+
+    println(s"Number of transactions: ${transactions.count}")
+
+    val model = new FPGrowth()
+      .setMinSupport(params.minSupport)
+      .setNumPartitions(params.numPartition)
+      .run(transactions)
 
-    // TODO: Read a user-specified input file.
-    val transactions = sc.parallelize(Seq(
-      "r z h k p",
-      "z y x w v u t s",
-      "s x o n r",
-      "x z y m t s q e",
-      "z",
-      "x z y r q t p").map(_.split(" ")), numSlices = 2)
-
-    val fpg = new FPGrowth()
-      .setMinSupport(0.3)
-    val model = fpg.run(transactions)
+    println(s"Number of frequent itemsets: ${model.freqItemsets.count}")
 
     model.freqItemsets.collect().foreach { itemset =>
       println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)