set findFirstMatchIn to ignore line if match not found- spark - scala - scala

Question

I am new to scala. I am trying to write a code to map parsed numbers in a series of xml files. My code works for a small RDD as below:
val myrdd = sc.parallelize(Array("FavoriteCount=\"23\" Score=\"43\"","FavoriteCount=\"12\" Score=\"32\"","FavoriteCount=\"32\" Score=\"2\""))
def successMatches(s: String): (String,Int) = {
val fcountMatcher = """FavoriteCount=\"(\d+)\"""".r
val scoreMatcher = """Score=\"(\d+)\"""".r
val fcount = fcountMatcher.findFirstMatchIn(s).get.group(1)
val score = scoreMatcher.findFirstMatchIn(s).get.group(1)
(fcount,score.toInt)
}
val myWords = myrdd.map(x => successMatches(x))
myWords.take(3)
myrdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[4] at parallelize at <console>:29
successMatches: (s: String)(String, Int)
myWords: Array[(String, Int)] = Array((23,43), (12,32), (32,2))
res1: Array[(String, Int)] = Array((23,43), (12,32), (32,2))
for the actual xml RDD it returns error message as below:
val myWords = valid_lines.take(1).map(x => successMatches(x))
myWords.take(1)
ava.util.NoSuchElementException: None.get
at scala.None$.get(Option.scala:347)
at scala.None$.get(Option.scala:345)
at successMatches(<console>:52)
at $anonfun$1.apply(<console>:57)
at $anonfun$1.apply(<console>:57)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
... 42 elided
What am I missing?
This is what the first element of the xml RDD looks like:
valid_lines.take(1)
res51: Array[String] = Array(" <row AnswerCount="0" Body="<p>I'm having trouble with a basic machine learning methodology question. I understand the concept of not using the same data to both train and evaluate a classifier, and furthermore when there are parameters in an algorithm to be optimized, you should use an independent third test set to get the final reportable performance figures (e.g. recall rate). However, using a <em>single</em> test set to measure performance seems to be problematic because the measures of performance will likely differ depending on how the data is partitioned into training (plus validation) and test sets, especially for small datasets. It would be better to average the results of N different partitions.</p>
<p>For t...