Github user actuaryzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/16630#discussion_r101158825
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
---
@@ -1152,4 +1170,32 @@ class GeneralizedLinearRegressionTrainingSummary
private[regression] (
"No p-value available for this GeneralizedLinearRegressionModel")
}
}
+
+ /**
+ * Summary table with feature name, coefficient, standard error,
+ * tValue and pValue.
+ */
+ @Since("2.2.0")
+ lazy val summaryTable: DataFrame = {
+ if (isNormalSolver) {
+ var featureNames = featureName
+ var coefficients = model.coefficients.toArray
+ var idx = Array.range(0, coefficients.length)
+ if (model.getFitIntercept) {
+ featureNames = featureNames :+ "Intercept"
+ coefficients = coefficients :+ model.intercept
+ // Reorder so that intercept comes first
+ idx = (coefficients.length - 1) +: idx
+ }
+ val result = for (i <- idx.toSeq) yield
+ (featureNames(i), coefficients(i), coefficientStandardErrors(i),
tValues(i), pValues(i))
+
+ val spark = SparkSession.builder().getOrCreate()
+ import spark.implicits._
--- End diff --
I was using the spark session and implicits to be able to use `toDF` to
create data frame with names from `Seq`. Could you explain how this `import
dataset.sparkSession.implicits._` works? Could not import it in spark shell.
```
<console>:56: error: not found: value dataset
import dataset.sparkSession.implicits._
```
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
