Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3637#discussion_r22983580
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -80,69 +50,157 @@ class LogisticRegression extends
Estimator[LogisticRegressionModel] with Logisti
def setRegParam(value: Double): this.type = set(regParam, value)
def setMaxIter(value: Int): this.type = set(maxIter, value)
- def setLabelCol(value: String): this.type = set(labelCol, value)
def setThreshold(value: Double): this.type = set(threshold, value)
- def setFeaturesCol(value: String): this.type = set(featuresCol, value)
- def setScoreCol(value: String): this.type = set(scoreCol, value)
- def setPredictionCol(value: String): this.type = set(predictionCol,
value)
override def fit(dataset: SchemaRDD, paramMap: ParamMap):
LogisticRegressionModel = {
+ // Check schema
transformSchema(dataset.schema, paramMap, logging = true)
- import dataset.sqlContext._
+
+ // Extract columns from data. If dataset is persisted, do not persist
oldDataset.
+ val oldDataset = extractLabeledPoints(dataset, paramMap)
val map = this.paramMap ++ paramMap
- val instances = dataset.select(map(labelCol).attr,
map(featuresCol).attr)
- .map { case Row(label: Double, features: Vector) =>
- LabeledPoint(label, features)
- }.persist(StorageLevel.MEMORY_AND_DISK)
+ val handlePersistence = dataset.getStorageLevel == StorageLevel.NONE
+ if (handlePersistence) {
+ oldDataset.persist(StorageLevel.MEMORY_AND_DISK)
+ }
+
+ // Train model
val lr = new LogisticRegressionWithLBFGS
lr.optimizer
.setRegParam(map(regParam))
.setNumIterations(map(maxIter))
- val lrm = new LogisticRegressionModel(this, map,
lr.run(instances).weights)
- instances.unpersist()
+ val oldModel = lr.run(oldDataset)
+ val lrm = new LogisticRegressionModel(this, map, oldModel.weights,
oldModel.intercept)
+
+ if (handlePersistence) {
+ oldDataset.unpersist()
+ }
+
// copy model params
Params.inheritValues(map, this, lrm)
lrm
}
- private[ml] override def transformSchema(schema: StructType, paramMap:
ParamMap): StructType = {
- validateAndTransformSchema(schema, paramMap, fitting = true)
- }
+ override protected def featuresDataType: DataType = new VectorUDT
}
+
/**
* :: AlphaComponent ::
+ *
* Model produced by [[LogisticRegression]].
*/
@AlphaComponent
class LogisticRegressionModel private[ml] (
override val parent: LogisticRegression,
--- End diff --
Currently, it's mainly for PipelineModel.getModel, which takes an Estimator
in the original Pipeline and returns the fitted Model produced by the Estimator.
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