ML Pipeline and metrics: Precision, Recall, AUC-ROC, F1Score - apache-spark

Question

I'm using ML Pipeline, something like:
VectorAssembler assembler = new VectorAssembler()
.setInputCols(columns)
.setOutputCol("features");
LogisticRegression lr = new LogisticRegression().setLabelCol(targetColumn);
lr.setMaxIter(10).setRegParam(0.01).setFeaturesCol("features");
Pipeline logisticRegression = new Pipeline();
logisticRegression.setStages(new PipelineStage[] {assembler, lr});
PipelineModel logisticRegressionModel = logisticRegression.fit(learningData);
What I want to is the way to get standard metric like Precision, Recall, AUC-ROC, F1-SCORE, ACCURACY on this model.
I've found BinaryClassificationMetrics - but not sure if it's compatible at all.
RegressionEvaluator seems to return only mse|rmse|r2|mae.
So what is the right way to extract Precision, Recall, etc with ML Pipeline?

Answer 1

Couple of things missing from Ryan's answer above.
I can confirm the following works (Note: my use case was for Multiclass Classification)
val scoredTestSet = model.transform(testSet)
val predictionLabelsRDD = scoredTestSet.select("prediction", "label").rdd.map(r => (r.getDouble(0), r.getDouble(1)))
val multiModelMetrics = new MulticlassMetrics(predictionAndLabelsRDD)

Answer 2

once you have scored data, get the prediction and label and pass that to BinaryClassificationMetrics
something like below (thought it's in scala I hope it helps)
val scoredTestSet = logisticRegressionModel.transform(testSet)
val predictionLabelsRDD = scoredTestSet.select("prediction", "label").map(r => (r.getDouble(0), r.getDouble(1)))
val binMetrics = new BinaryClassificationMetrics(predictionAndLabels)
// binMetrics.areaUnderROC
other examples from https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html#binary-classification
prediction in this case is 1.0 or 0.0
you can also extract the probability and use that instead of the prediction so that binMetrics can show data for multiple thresholds