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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,下面请大家跟着丸趣 TV 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)
})
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