与Is it possible to access estimator attributes in spark.ml pipelines?类似我想访问估计器,例如管道中的最后一个元素。管道中的火花访问估计器
这里提到的方法似乎不再适用于spark 2.0.1。它现在如何工作?
编辑
也许我应该解释多一点点细节: 这里是我的估计+矢量汇编:
val numRound = 20
val numWorkers = 4
val xgbBaseParams = Map(
"max_depth" -> 10,
"eta" -> 0.1,
"seed" -> 50,
"silent" -> 1,
"objective" -> "binary:logistic"
)
val xgbEstimator = new XGBoostEstimator(xgbBaseParams)
.setFeaturesCol("features")
.setLabelCol("label")
val vectorAssembler = new VectorAssembler()
.setInputCols(train.columns
.filter(!_.contains("label")))
.setOutputCol("features")
val simplePipeParams = new ParamGridBuilder()
.addGrid(xgbEstimator.round, Array(numRound))
.addGrid(xgbEstimator.nWorkers, Array(numWorkers))
.build()
val simplPipe = new Pipeline()
.setStages(Array(vectorAssembler, xgbEstimator))
val numberOfFolds = 2
val cv = new CrossValidator()
.setEstimator(simplPipe)
.setEvaluator(new BinaryClassificationEvaluator()
.setLabelCol("label")
.setRawPredictionCol("prediction"))
.setEstimatorParamMaps(simplePipeParams)
.setNumFolds(numberOfFolds)
.setSeed(gSeed)
val cvModel = cv.fit(train)
val trainPerformance = cvModel.transform(train)
val testPerformance = cvModel.transform(test)
现在我想例如执行自定义得分!= 0.5
截止点。这是可能的,如果我弄到模型:
val realModel = cvModel.bestModel.asInstanceOf[XGBoostClassificationModel]
但这一步这里不编译。 感谢您的建议,我可以得到模型:
val pipelineModel: Option[PipelineModel] = cvModel.bestModel match {
case p: PipelineModel => Some(p)
case _ => None
}
val realModel: Option[XGBoostClassificationModel] = pipelineModel
.flatMap {
_.stages.collect { case t: XGBoostClassificationModel => t }
.headOption
}
// TODO write it nicer
val measureResults = realModel.map {
rm =>
{
for (
thresholds <- Array(Array(0.2, 0.8), Array(0.3, 0.7), Array(0.4, 0.6),
Array(0.6, 0.4), Array(0.7, 0.3), Array(0.8, 0.2))
) {
rm.setThresholds(thresholds)
val predResult = rm.transform(test)
.select("label", "probabilities", "prediction")
.as[LabelledEvaluation]
println("cutoff was ", thresholds)
calculateEvaluation(R, predResult)
}
}
}
然而,问题是,
val predResult = rm.transform(test)
会train
失败不包含vectorAssembler
的功能列。 此列仅在完整管道运行时创建。
所以我决定创建第二个管道:
val scoringPipe = new Pipeline()
.setStages(Array(vectorAssembler, rm))
val predResult = scoringPipe.fit(train).transform(test)
,但似乎有点笨拙。你有更好/更好的想法吗?
我相信你正在寻找的是'pipeline.getStages()'返回一个数组的形式所有阶段。然后你可以访问你想要的任何阶段。 [Documentation]中的更多信息(http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.Pipeline)。 – ShirishT
[如何从交叉验证器获得训练出的最佳模型]可能的重复(http://stackoverflow.com/questions/36347875/how-to-obtain-the-trained-best-model-from-a-crossvalidator) – 2016-11-12 00:46:00