Spark Logistic regression and metrics - scala

Question

I want to run logistic regression 100 times with random splitting into test and training. I want to then save the performance metrics for individual runs and then later use them for gaining insight into the performance.
for (index <- 1 to 100) {
val splits = training_data.randomSplit(Array(0.90, 0.10), seed = index)
val training = splits(0).cache()
val test = splits(1)
logrmodel = train_LogisticRegression_model(training)
performLogisticRegressionRuns(logrmodel, test, index)
}
spark.stop()
}
def performLogisticRegressionRuns(model: LogisticRegressionModel, test: RDD[LabeledPoint], iterationcount: Int) {
private val sb = StringBuilder.newBuilder
// Compute raw scores on the test set. Once I cle
model.clearThreshold()
val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
val prediction = model.predict(features)
(prediction, label)
}
val bcmetrics = new BinaryClassificationMetrics(predictionAndLabels)
// I am showing two sample metrics, but I am collecting more including recall, area under roc, f1 score etc....
val precision = bcmetrics.precisionByThreshold()
precision.foreach { case (t, p) =>
// If threshold is 0.5 as what we want, then get the precision and append it to the string. Idea is if score is <0.5 class 0, else class 1.
if (t == 0.5) {
println(s"Threshold is: $t, Precision is: $p")
sb ++= p.toString() + "\t"
}
}
val auROC = bcmetrics.areaUnderROC
sb ++= iteration + auPRC.toString() + "\t"
I want to save the performance results of each iteration in separate file. I tried this, but it does not work, any help with this will be great
val data = spark.parallelize(sb)
val filename = "logreg-metrics" + iterationcount.toString() + ".txt"
data.saveAsTextFile(filename)
}

Answer 1

I was able to resolve this, I did the following. I converted the String to a list.
val data = spark.parallelize(List(sb))
val filename = "logreg-metrics" + iterationcount.toString() + ".txt"
data.saveAsTextFile(filename)