Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/8197#discussion_r37136987
--- Diff: docs/ml-guide.md ---
@@ -801,6 +801,141 @@ jsc.stop();
</div>
+## Examples: Summaries for LogisticRegression.
+
+Once LogisticRegression is run on data, it is useful to extract statistics
such as the
+loss per iteration which will provide an intuition on overfitting and
metrics to understand
+how well the model has performed on training and test data.
+
+LogisticRegressionTrainingSummary provides an interface to access such
relevant information
+i.e the objectiveHistory and metrics to evaluate the performance on the
training data
+directly with very less code to be rewritten by the user.
+In the future, a method would be made available in the fitted
LogisticRegressionModel to obtain
+a LogisticRegressionSummary of the test data as well.
+
+This examples illustrates the use of LogisticRegressionTrainingSummary on
some toyData.
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.ml.classification.{LogisticRegression,
BinaryLogisticRegressionSummary}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.sql.{Row, SQLContext}
+
+val conf = new SparkConf().setAppName("LogisticRegressionSummary")
+val sc = new SparkContext(conf)
+val sqlContext = new SQLContext(sc)
+import sqlContext.implicits._
+
+// Use some random data for demonstration.
+// Note that the RDD of LabeledPoints can be converted to a dataframe
directly.
+val data = sc.parallelize(Array(
+ LabeledPoint(0.0, Vectors.dense(0.2, 4.5, 1.6)),
+ LabeledPoint(1.0, Vectors.dense(3.1, 6.8, 3.6)),
+ LabeledPoint(0.0, Vectors.dense(2.4, 0.9, 1.9)),
+ LabeledPoint(1.0, Vectors.dense(9.1, 3.1, 3.6)),
+ LabeledPoint(0.0, Vectors.dense(2.5, 1.9, 9.1)))
+)
+val logRegDataFrame = data.toDF()
+
+// Run Logistic Regression on your toy data.
+// Since LogisticRegression is an estimator, it returns an instance of
LogisticRegressionModel
+// which is a transformer.
+val logReg = new LogisticRegression()
+logReg.setMaxIter(5)
+logReg.setRegParam(0.01)
+val logRegModel = logReg.fit(logRegDataFrame)
+
+// Extract the summary directly from the returned LogisticRegressionModel
instance.
+val trainingSummary = logRegModel.summary
+
+// Obtain the loss per iteration. This should decrease upto a certain
point and
+// then increase or show negligible change after this.
+val objectiveHistory = trainingSummary.objectiveHistory
+objectiveHistory.foreach(loss => println(loss))
+
+// Obtain the metrics useful to judge performance on test data.
+val binarySummary =
trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary]
+
+// Obtain the receiver-operating characteristic as a dataframe and
areaUnderROC.
+val roc = binarySummary.roc
+val truePositiveRate = roc.select("FPR")
+val area = binarySummary.areaUnderROC
+
+// Obtain the threshold with the highest fMeasure.
+val fMeasure = binarySummary.fMeasureByThreshold
+val fScoreRDD = fMeasure.map { case Row(thresh: Double, fscore: Double) =>
(thresh, fscore) }
+val (highThresh, highFScore) = fScoreRDD.fold((0.0, 0.0))((threshFScore1,
threshFScore2) => {
+ if (threshFScore1._2 > threshFScore2._2) threshFScore1 else threshFScore2
+})
--- End diff --
The closest I could come up with is.
val maxFMeasure =
fMeasure.select(max(df("F-Measure"))).collect()(0).getFloat(0)
val threshold = fMeasure.filter(df("threshold")).collect()(0).getFloat(0)
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