[SPARK-30398][ML] PCA/RegressionMetrics/RowMatrix avoid unnecessary computation

### What changes were proposed in this pull request?
use `.ml.Summarizer` instead of `.mllib.MultivariateOnlineSummarizer` to avoid computation of unused metrics

### Why are the changes needed?
to avoid computation of unused metrics

### Does this PR introduce any user-facing change?
No

### How was this patch tested?
existing testsuites

Closes #27059 from zhengruifeng/pac_summarizer.

Authored-by: zhengruifeng <[email protected]>
Signed-off-by: Sean Owen <[email protected]>

Author: zhengruifeng

mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala

@@ -32,7 +32,7 @@ import org.apache.spark.ml.optim.aggregator.HingeAggregator
 import org.apache.spark.ml.optim.loss.{L2Regularization, RDDLossFunction}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.stat.SummaryBuilderImpl._
+import org.apache.spark.ml.stat._
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.sql.{Dataset, Row}
@@ -170,7 +170,7 @@ class LinearSVC @Since("2.2.0") (
       regParam, maxIter, fitIntercept, tol, standardization, threshold, aggregationDepth)
 
     val (summarizer, labelSummarizer) = instances.treeAggregate(
-      (createSummarizerBuffer("mean", "std", "count"), new MultiClassSummarizer))(
+      (Summarizer.createSummarizerBuffer("mean", "std", "count"), new MultiClassSummarizer))(
       seqOp = (c: (SummarizerBuffer, MultiClassSummarizer), instance: Instance) =>
         (c._1.add(instance.features, instance.weight), c._2.add(instance.label, instance.weight)),
       combOp = (c1: (SummarizerBuffer, MultiClassSummarizer),

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -34,7 +34,7 @@ import org.apache.spark.ml.optim.aggregator.LogisticAggregator
 import org.apache.spark.ml.optim.loss.{L2Regularization, RDDLossFunction}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.stat.SummaryBuilderImpl._
+import org.apache.spark.ml.stat._
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.evaluation.{BinaryClassificationMetrics, MulticlassMetrics}
@@ -501,7 +501,7 @@ class LogisticRegression @Since("1.2.0") (
       fitIntercept)
 
     val (summarizer, labelSummarizer) = instances.treeAggregate(
-      (createSummarizerBuffer("mean", "std", "count"), new MultiClassSummarizer))(
+      (Summarizer.createSummarizerBuffer("mean", "std", "count"), new MultiClassSummarizer))(
       seqOp = (c: (SummarizerBuffer, MultiClassSummarizer), instance: Instance) =>
         (c._1.add(instance.features, instance.weight), c._2.add(instance.label, instance.weight)),
       combOp = (c1: (SummarizerBuffer, MultiClassSummarizer),

mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala

@@ -31,7 +31,7 @@ import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.stat.SummaryBuilderImpl._
+import org.apache.spark.ml.stat._
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.util.MLUtils
@@ -215,7 +215,7 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 
     val featuresSummarizer = instances.treeAggregate(
-      createSummarizerBuffer("mean", "std", "count"))(
+      Summarizer.createSummarizerBuffer("mean", "std", "count"))(
       seqOp = (c: SummarizerBuffer, v: AFTPoint) => c.add(v.features),
       combOp = (c1: SummarizerBuffer, c2: SummarizerBuffer) => c1.merge(c2),
       depth = $(aggregationDepth)

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -36,7 +36,7 @@ import org.apache.spark.ml.optim.aggregator.{HuberAggregator, LeastSquaresAggreg
 import org.apache.spark.ml.optim.loss.{L2Regularization, RDDLossFunction}
 import org.apache.spark.ml.param.{DoubleParam, Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.stat.SummaryBuilderImpl._
+import org.apache.spark.ml.stat._
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.evaluation.RegressionMetrics
@@ -358,8 +358,8 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 
     val (featuresSummarizer, ySummarizer) = instances.treeAggregate(
-      (createSummarizerBuffer("mean", "std"),
-        createSummarizerBuffer("mean", "std", "count")))(
+      (Summarizer.createSummarizerBuffer("mean", "std"),
+        Summarizer.createSummarizerBuffer("mean", "std", "count")))(
       seqOp = (c: (SummarizerBuffer, SummarizerBuffer), instance: Instance) =>
         (c._1.add(instance.features, instance.weight),
           c._2.add(Vectors.dense(instance.label), instance.weight)),