Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4233#discussion_r23909298
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
---
@@ -68,6 +79,65 @@ class LogisticRegressionModel (
case None => score
}
}
+
+ override def save(sc: SparkContext, path: String): Unit = {
+ val sqlContext = new SQLContext(sc)
+ import sqlContext._
+
+ // Create JSON metadata.
+ val metadata = LogisticRegressionModel.Metadata(
+ clazz = this.getClass.getName, version = Exportable.latestVersion)
+ val metadataRDD: DataFrame = sc.parallelize(Seq(metadata))
+ metadataRDD.toJSON.saveAsTextFile(path + "/metadata")
+ // Create Parquet data.
+ val data = LogisticRegressionModel.Data(weights, intercept, threshold)
+ val dataRDD: DataFrame = sc.parallelize(Seq(data))
+ dataRDD.saveAsParquetFile(path + "/data")
+ }
+}
+
+object LogisticRegressionModel extends Importable[LogisticRegressionModel]
{
+
+ private case class Metadata(clazz: String, version: String)
+
+ private case class Data(weights: Vector, intercept: Double, threshold:
Option[Double])
+
+ override def load(sc: SparkContext, path: String):
LogisticRegressionModel = {
+ val sqlContext = new SQLContext(sc)
+ import sqlContext._
+
+ // Load JSON metadata.
+ val metadataRDD = sqlContext.jsonFile(path + "/metadata")
--- End diff --
Also, I get the motivation for using json4s directly rather than going
through DataFrame and DataFrame.toJSON in terms of reducing dependencies.
However, I like the idea of using DataFrame since it will be helpful when we
add other types of metadata, such as info about each feature.
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