这篇文章主要讲解了“Spark ALS实现的步骤是什么”，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习“Spark ALS实现的步骤是什么”吧！
spark ALS算法是做个性推荐用的，它所需要的数据集是类似用户对商品的打分表之类的数据集。实现步骤主要以下几步：
1、定义输入数据
2、输入数据转换成评分数据格式，如case class Rating(user: Int, movie: Int, rating: Float)
3、设计ALS模型训练数据
4、计算推荐数据，存储起来供业务系统直接使用。
下面看看具体的代码：
package recommend
import org.apache.spark.sql.SparkSession
import java.util.Properties
import org.apache.spark.rdd.RDD
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.recommendation.ALS
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.Row
import org.apache.spark.ml.feature.IndexToString
import scala.collection.mutable.ArrayBuffer
import org.apache.spark.TaskContext
import org.apache.spark.ml.Pipeline
import org.apache.spark.sql.SaveMode
/**
* 个性化推荐ALS算法
* 用户对资源的点击率作为评分
*
*/
object Recommend {
case class Rating(user: Int, movie: Int, rating: Float)
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("Java Spark MYSQL Recommend")
.master("local")
.config("es.nodes", "127.0.0.1")
.config("es.port", "9200")
.config("es.mapping.date.rich", "false") //不解析日期类型
.getOrCreate()
trainModel(spark)
spark.close()
}
def trainModel(spark: SparkSession): Unit = {
import spark.implicits._
val MAX = 3 // 最大推荐数目
val rank = 10 // 向量大小，默认10
val iterations = 10 // 迭代次数，默认10
val url = "jdbc:mysql://127.0.0.1:3306/test?useUnicode=true&characterEncoding=utf8"
val table = "clicks"
val user = "root"
val pass = "123456"
val props = new Properties()
props.setProperty("user", user) // 设置用户名
props.setProperty("password", pass) // 设置密码
val clicks = spark.read.jdbc(url, table, props).repartition(4)
clicks.createOrReplaceGlobalTempView("clicks")
val agg = spark.sql("SELECT userId ,resId ,COUNT(id) AS clicks FROM global_temp.clicks GROUP BY userId,resId")
val userIndexer = new StringIndexer()
.setInputCol("userId")
.setOutputCol("userIndex")
val resIndexer = new StringIndexer()
.setInputCol("resId")
.setOutputCol("resIndex")
val indexed1 = userIndexer.fit(agg).transform(agg)
val indexed2 = resIndexer.fit(indexed1).transform(indexed1)
indexed2.show()
val ratings = indexed2.map(x => Rating(x.getDouble(3).toInt, x.getDouble(4).toInt, x.getLong(2).toFloat))
ratings.show()
val Array(training, test) = ratings.randomSplit(Array(0.9, 0.1))
println("training:")
training.show()
println("test:")
test.show()
//隐性反馈和显示反馈
val als = new ALS()
.setMaxIter(iterations)
.setRegParam(0.01)
.setImplicitPrefs(false)
.setUserCol("user")
.setItemCol("movie")
.setRatingCol("rating")
val model = als.fit(ratings)
// Evaluate the model by computing the RMSE on the test data
// Note we set cold start strategy to 'drop' to ensure we don't get NaN evaluation metrics
model.setColdStartStrategy("drop")
val predictions = model.transform(test)
val r2 = model.recommendForAllUsers(MAX)
println(r2.schema)
val result = r2.rdd.flatMap(row => {
val userId = row.getInt(0)
val arrayPredict: Seq[Row] = row.getSeq(1)
var result = ArrayBuffer[Rating]()
arrayPredict.foreach(rowPredict => {
val p = rowPredict(0).asInstanceOf[Int]
val score = rowPredict(1).asInstanceOf[Float]
val sql = "insert into recommends(userId,resId,score) values (" +
userId + "," +
rowPredict(0) + "," +
rowPredict(1) +
")"
println("sql:" + sql)
result.append(Rating(userId, p, score))
})
for (i <- result) yield {
i
}
})
println("推荐结果RDD已展开")
result.toDF().show()
//资源id隐射
val resInt2Index = new IndexToString()
.setInputCol("movie")
.setOutputCol("resId")
.setLabels(resIndexer.fit(indexed1).labels)
//userId映射
val userInt2Index = new IndexToString()
.setInputCol("user")
.setOutputCol("userId")
.setLabels(userIndexer.fit(agg).labels)
val rc = userInt2Index.transform(resInt2Index.transform(result.toDF()))
rc.show()
rc.withColumnRenamed("rating","score").select("userId", "resId","score").write.mode(SaveMode.Overwrite)
.format("jdbc")
.option("url", url)
.option("dbtable", "recommends")
.option("user", user)
.option("password", pass)
.option("batchsize", "5000")
.option("truncate", "true")
.save
println("finished!!!")
}
}
DataFrame写入mysql还有另一种写法，就是原生写入：
//分区写推荐结果到mysql
r2.foreachPartition(p => {
@transient val conn = ConnectionPool.getConnection
p.foreach(row => {
val userId = row.getInt(0)
val arrayPredict: Seq[Row] = row.getSeq(1)
arrayPredict.foreach(rowPredict => {
println(rowPredict(0) + "@" + rowPredict(1))
val sql = "insert into recommends(userId,resId,score) values (" +
userId+"," +
rowPredict(0)+","+
rowPredict(1) +
")"
println("sql:"+sql)
val stmt = conn.createStatement
stmt.executeUpdate(sql)
})
})
ConnectionPool.returnConnection(conn)
})
感谢各位的阅读，以上就是“Spark ALS实现的步骤是什么”的内容了，经过本文的学习后，相信大家对Spark ALS实现的步骤是什么这一问题有了更深刻的体会，具体使用情况还需要大家实践验证。这里是云，小编将为大家推送更多相关知识点的文章，欢迎关注！
