Github user actuaryzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/16630#discussion_r103004564
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
---
@@ -1152,4 +1173,33 @@ 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 featureNamesLocal = featureNames
+ var coefficients = model.coefficients.toArray
+ var idx = Array.range(0, coefficients.length)
+ if (model.getFitIntercept) {
+ featureNamesLocal = featureNamesLocal :+ Intercept
+ coefficients = coefficients :+ model.intercept
+ // Reorder so that intercept comes first
+ idx = (coefficients.length - 1) +: idx
+ }
+ val result = for (i <- idx.toSeq) yield
+ (featureNamesLocal(i), coefficients(i),
coefficientStandardErrors(i),
+ tValues(i), pValues(i))
+
+ val spark = SparkSession.builder().getOrCreate()
--- End diff --
@felixcheung Would you elaborate on your concern here? The reason I create
the SparkSession is to use convert `Seq` to `DataFrame` using `toDF`. Is there
a way we can create data frame without explicitly using spark session? Thanks.
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