本文介绍了Spark MLlib ALS中的非整数ID的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我想使用
val ratings = data.map(_.split(',') match {
case Array(user,item,rate)
=>
Rating(user.toInt,item.toInt,rate.toFloat)
})
val model = ALS.train(ratings,rank,numIterations,alpha)
但是,我得到的用户数据存储为Long.切换为int时,可能会产生错误.我该怎么解决这个问题?
However, the user data i get are stored as Long. When switched to int, it may produce error.How can i do to solve the problem?
推荐答案
您可以使用支持Long
标签的ML实现之一. RDD
版本,与其他实现相比,它的用户友好性大大降低:
You can use one of ML implementations which support Long
labels. RDD
version it is significantly less user friendly compared to other implementations:
import org.apache.spark.ml.recommendation.ALS
import org.apache.spark.ml.recommendation.ALS.Rating
val ratings = sc.parallelize(Seq(Rating(1L, 2L, 3.0f), Rating(2L, 3L, 5.0f)))
val (userFactors, itemFactors) = ALS.train(ratings)
仅返回因子,而DataFrame
版本返回模型:
and returns only factors but DataFrame
version returns a model:
val ratingsDF= ratings.toDF
val alsModel = new ALS().fit(ratingsDF)
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