2017-07-20 23 views
2

我有一个用LinearRegression处理的DataFrame。如果我做它直接,像下面,我可以显示模型的细节:如何访问ML管道中基础模型的参数?

val lr = new LinearRegression() 
val lrModel = lr.fit(df) 

lrModel: org.apache.spark.ml.regression.LinearRegressionModel = linReg_b22a7bb88404 

println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}") 
Coefficients: [0.9705748115939526] Intercept: 0.31041486689532866 

然而,如果I(在下面的简化示例等)使用它管线内部,

val pipeline = new Pipeline().setStages(Array(lr)) 
val lrModel = pipeline.fit(df) 

那么我得到以下错误。

scala> lrModel 
res9: org.apache.spark.ml.PipelineModel = pipeline_99ca9cba48f8 

scala> println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}") 
<console>:68: error: value coefficients is not a member of org.apache.spark.ml.PipelineModel 
     println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}") 
             ^
<console>:68: error: value intercept is not a member of org.apache.spark.ml.PipelineModel 
     println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}") 

我明白这意味着什么(很明显我有一个不同的类,因为管道的),但不知道怎么去真正的底层模型。

回答

2

LinearRegressionModel应位于stages之内,其索引与其对应的LinearRegression完全相同。

import org.apache.spark.ml.regressio‌​n.LinearRegressionMo‌​del 
lrModel.stages(0).asInstanceOf[LinearRegressionMo‌​del]