火花- Prediction.io - scala.MatchError: null
火花- Prediction.io - scala.MatchError: null
提问于 2015-03-31 16:37:56
我正在为prediction.io开发一个模板，我遇到了火花的麻烦。
我一直收到一个scala.MatchError错误：这里的全部要点
scala.MatchError: null
at org.apache.spark.rdd.PairRDDFunctions.lookup(PairRDDFunctions.scala:831)
at org.apache.spark.mllib.recommendation.MatrixFactorizationModel.predict(MatrixFactorizationModel.scala:66)
at org.template.prediction.ALSAlgorithm$$anonfun$predict$1$$anonfun$apply$1.apply(ALSAlgorithm.scala:86)
at org.template.prediction.ALSAlgorithm$$anonfun$predict$1$$anonfun$apply$1.apply(ALSAlgorithm.scala:79)
at scala.Option.map(Option.scala:145)
at org.template.prediction.ALSAlgorithm$$anonfun$predict$1.apply(ALSAlgorithm.scala:79)
at org.template.prediction.ALSAlgorithm$$anonfun$predict$1.apply(ALSAlgorithm.scala:78)代码github源
val usersWithCounts =
ratingsRDD
.map(r => (r.user, (1, Seq[Rating](Rating(r.user, r.item, r.rating)))))
.reduceByKey((v1, v2) => (v1._1 + v2._1, v1._2.union(v2._2)))
.filter(_._2._1 >= evalK)
// create evalK folds of ratings
(0 until evalK).map { idx =>
// start by getting this fold's ratings for each user
val fold = usersWithCounts
.map { userKV =>
val userRatings = userKV._2._2.zipWithIndex
val trainingRatings = userRatings.filter(_._2 % evalK != idx).map(_._1)
val testingRatings = userRatings.filter(_._2 % evalK == idx).map(_._1)
(trainingRatings, testingRatings) // split the user's ratings into a training set and a testing set
}
.reduce((l, r) => (l._1.union(r._1), l._2.union(r._2))) // merge all the testing and training sets into a single testing and training set
val testingSet = fold._2.map {
r => (new Query(r.user, r.item), new ActualResult(r.rating))
}
(
new TrainingData(sc.parallelize(fold._1)),
new EmptyEvaluationInfo(),
sc.parallelize(testingSet)
)
}为了进行评估，我需要将评分分成一个培训组和一个测试组。为了确保每个用户都作为培训的一部分被包括在内，我将所有用户的评级组合在一起，然后对每个用户进行拆分，然后将拆分合并在一起。
也许有更好的方法来做这件事？
回答 1
Stack Overflow用户
回答已采纳
发布于 2015-03-31 19:41:34
该错误意味着userFeatures of MLlib MatrixFactorizationModel不包含用户id (例如，如果用户不在训练数据中)。MLlib在查找后不会检查这一点(使用.head)：https://github.com/apache/spark/blob/v1.2.0/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala#L66
要进行调试，可以实现修改后的model.predict()版本，以检查模型中是否存在userId/itemId，而不是调用默认的：
val itemScore = model.predict(userInt, itemInt) 更改为使用.headOption：
val itemScore = model.userFeatures.lookup(userInt).headOption.map { userFeature =>
model.productFeatures.lookup(itemInt).headOption.map { productFeature =>
val userVector = new DoubleMatrix(userFeature)
val productVector = new DoubleMatrix(productFeature)
userVector.dot(productVector)
}.getOrElse{
logger.info(s"No itemFeature for item ${query.item}.")
0.0 // return default score
}
}.getOrElse{
logger.info(s"No userFeature for user ${query.user}.")
0.0 // return default score
}页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/29373873
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