[SPARK-15457][MLLIB][ML] Eliminate some warnings from MLlib about deprecations

## What changes were proposed in this pull request?

Several classes and methods have been deprecated and are creating lots of build warnings in branch-2.0. This issue is to identify and fix those items:
* WithSGD classes: Change to make class not deprecated, object deprecated, and public class constructor deprecated. Any public use will require a deprecated API. We need to keep a non-deprecated private API since we cannot eliminate certain uses: Python API, streaming algs, and examples.
  * Use in PythonMLlibAPI: Change to using private constructors
  * Streaming algs: No warnings after we un-deprecate the classes
  * Examples: Deprecate or change ones which use deprecated APIs
* MulticlassMetrics fields (precision, etc.)
* LinearRegressionSummary.model field

## How was this patch tested?

Existing tests.  Checked for warnings manually.

Author: Sean Owen <sowen@cloudera.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #13314 from jkbradley/warning-cleanups.

11 files changed, 19 insertions, 23 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java
index 9d8e4a90db..7fc371ec0f 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java
@@ -65,8 +65,8 @@ public class JavaLogisticRegressionWithLBFGSExample {
 
     // Get evaluation metrics.
     MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd());
-    double precision = metrics.precision();
-    System.out.println("Precision = " + precision);
+    double accuracy = metrics.accuracy();
+    System.out.println("Accuracy = " + accuracy);
 
     // Save and load model
     model.save(sc, "target/tmp/javaLogisticRegressionWithLBFGSModel");
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java
index 5247c9c748..e84a3a712d 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java
@@ -68,9 +68,7 @@ public class JavaMulticlassClassificationMetricsExample {
     System.out.println("Confusion matrix: \n" + confusion);
 
     // Overall statistics
-    System.out.println("Precision = " + metrics.precision());
-    System.out.println("Recall = " + metrics.recall());
-    System.out.println("F1 Score = " + metrics.fMeasure());
+    System.out.println("Accuracy = " + metrics.accuracy());
 
     // Stats by labels
     for (int i = 0; i < metrics.labels().length; i++) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
index 5e51dbad76..de4474555d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
@@ -321,7 +321,7 @@ object DecisionTreeExample {
       case None => throw new RuntimeException(
         "Unknown failure when indexing labels for classification.")
     }
-    val accuracy = new MulticlassMetrics(predictions.zip(labels)).precision
+    val accuracy = new MulticlassMetrics(predictions.zip(labels)).accuracy
     println(s"  Accuracy ($numClasses classes): $accuracy")
   }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index ee811d3aa1..a85aa2cac9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -295,11 +295,10 @@ object DecisionTreeRunner {
       }
       if (params.algo == Classification) {
         val trainAccuracy =
-          new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label)))
-            .precision
+          new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label))).accuracy
         println(s"Train accuracy = $trainAccuracy")
         val testAccuracy =
-          new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision
+          new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).accuracy
         println(s"Test accuracy = $testAccuracy")
       }
       if (params.algo == Regression) {
@@ -322,11 +321,10 @@ object DecisionTreeRunner {
           println(model) // Print model summary.
         }
         val trainAccuracy =
-          new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label)))
-            .precision
+          new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label))).accuracy
         println(s"Train accuracy = $trainAccuracy")
         val testAccuracy =
-          new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision
+          new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).accuracy
         println(s"Test accuracy = $testAccuracy")
       }
       if (params.algo == Regression) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
index b0144ef533..90e4687c1f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala
@@ -120,11 +120,10 @@ object GradientBoostedTreesRunner {
         println(model) // Print model summary.
       }
       val trainAccuracy =
-        new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label)))
-          .precision
+        new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label))).accuracy
       println(s"Train accuracy = $trainAccuracy")
       val testAccuracy =
-        new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision
+        new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).accuracy
       println(s"Test accuracy = $testAccuracy")
     } else if (params.algo == "Regression") {
       val startTime = System.nanoTime()
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
index f87611f5d4..a70203028c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala
@@ -34,6 +34,7 @@ import org.apache.spark.mllib.util.MLUtils
  * A synthetic dataset can be found at `data/mllib/sample_linear_regression_data.txt`.
  * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
  */
+@deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
 object LinearRegression {
 
   object RegType extends Enumeration {
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala
index 669868787e..d399618094 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala
@@ -26,6 +26,7 @@ import org.apache.spark.mllib.regression.LinearRegressionModel
 import org.apache.spark.mllib.regression.LinearRegressionWithSGD
 // $example off$
 
+@deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
 object LinearRegressionWithSGDExample {
 
   def main(args: Array[String]): Unit = {
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala
index 632a2d537e..31ba740ad4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala
@@ -54,8 +54,8 @@ object LogisticRegressionWithLBFGSExample {
 
     // Get evaluation metrics.
     val metrics = new MulticlassMetrics(predictionAndLabels)
-    val precision = metrics.precision
-    println("Precision = " + precision)
+    val accuracy = metrics.accuracy
+    println(s"Accuracy = $accuracy")
 
     // Save and load model
     model.save(sc, "target/tmp/scalaLogisticRegressionWithLBFGSModel")
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala
index 4f925ede24..12394c867e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala
@@ -59,13 +59,9 @@ object MulticlassMetricsExample {
     println(metrics.confusionMatrix)
 
     // Overall Statistics
-    val precision = metrics.precision
-    val recall = metrics.recall // same as true positive rate
-    val f1Score = metrics.fMeasure
+    val accuracy = metrics.accuracy
     println("Summary Statistics")
-    println(s"Precision = $precision")
-    println(s"Recall = $recall")
-    println(s"F1 Score = $f1Score")
+    println(s"Accuracy = $accuracy")
 
     // Precision by label
     val labels = metrics.labels
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
index f7a8136953..eb36697d94 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala
@@ -26,6 +26,7 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}
 // $example off$
 
+@deprecated("Deprecated since LinearRegressionWithSGD is deprecated.  Use ml.feature.PCA", "2.0.0")
 object PCAExample {
 
   def main(args: Array[String]): Unit = {
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
index abeaaa00b5..76cfb804e1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
@@ -25,6 +25,8 @@ import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}
 // $example off$
 import org.apache.spark.sql.SparkSession
 
+@deprecated("Use ml.regression.LinearRegression and the resulting model summary for metrics",
+  "2.0.0")
 object RegressionMetricsExample {
   def main(args: Array[String]): Unit = {
     val spark = SparkSession