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March 5, 2016 03:24
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Spark Mllib K-means example
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| // k-mean | |
| import org.apache.spark.mllib.clustering.{KMeans, KMeansModel} | |
| import org.apache.spark.mllib.linalg.Vectors | |
| import org.apache.spark.mllib.regression.LabeledPoint | |
| case class Data(userId: String, videoId: String, isKid: String) | |
| val raw_data = sc.textFile("/Users/zzchen/Downloads/locked_video_up5.csv").map { line => | |
| val f = line.split(",") | |
| Data(f(0),f(1).split("=")(1),f(5)) | |
| } | |
| /* | |
| val df = raw_data.toDF().groupBy("_1").pivot("_2").count() | |
| df.show() | |
| */ | |
| raw_data.map(_.userId).distinct().zipWithUniqueId().map{ case (a, b) => (a, b)}.toDF().registerTempTable("user") | |
| raw_data.toDF().registerTempTable("raw") | |
| //+--------------------+---+--------------------+-----------+-----+ | |
| //| _1| _2| userId| videoId|isKid| | |
| //+--------------------+---+--------------------+-----------+-----+ | |
| //|ooxxxxxxxxxxxxxx@...| 6|ooxxxxxxxxxxxxxx@...|ToKMrLuH_iM| Yes| | |
| //+--------------------+---+--------------------+-----------+-----+ | |
| val joined_data = sqlContext.sql("SELECT * FROM user u JOIN raw r ON u.`_1` = r.userId") | |
| val df3 = joined_data.groupBy("_2").pivot("videoId").count() | |
| val input_data = df3.map{r => | |
| val ary = r.toSeq.map({ case col: Long => col.toDouble }).toArray | |
| Vectors.dense(ary.slice(1, ary.length).map(s=>s)) | |
| //Vectors.dense(ary) | |
| //(ary.slice(1, ary.length).map(s=>s)) | |
| //println(ary.slice(1, ary.length)) | |
| } | |
| df3.write.format("com.databricks.spark.csv").option("header", "true").save("GGGG.csv") | |
| val numClusters = 6 | |
| val numIterations = 20 | |
| val clusters = KMeans.train(input_data, numClusters, numIterations) | |
| val WSSSE = clusters.computeCost(input_data) | |
| input_data.map{ in => | |
| println("Predict: " + in + " : " + clusters.predict(in)) | |
| }.collect() | |
| clusters.predict(Vectors.dense(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)) | |
| clusters.predict(Vectors.dense(0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)) |
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