[SPARK-16485][ML][DOC] Fix privacy of GLM members, rename sqlDataTypes for ML, doc fixes

## What changes were proposed in this pull request?

Fixing issues found during 2.0 API checks:
* GeneralizedLinearRegressionModel: linkObj, familyObj, familyAndLink should not be exposed
* sqlDataTypes: name does not follow conventions. Do we need to expose it?
* Evaluator: inconsistent doc between evaluate and isLargerBetter
* MinMaxScaler: math rendering --> hard to make it great, but I'll change it a little
* GeneralizedLinearRegressionSummary: aic doc is incorrect --> will change to use more common name

## How was this patch tested?

Existing unit tests.  Docs generated locally.  (MinMaxScaler is improved a tiny bit.)

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #14187 from jkbradley/final-api-check-2.0.

mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala

@@ -30,7 +30,8 @@ import org.apache.spark.sql.Dataset
 abstract class Evaluator extends Params {
 
   /**
-   * Evaluates model output and returns a scalar metric (larger is better).
+   * Evaluates model output and returns a scalar metric.
+   * The value of [[isLargerBetter]] specifies whether larger values are better.
    *
    * @param dataset a dataset that contains labels/observations and predictions.
    * @param paramMap parameter map that specifies the input columns and output metrics
@@ -42,7 +43,9 @@ abstract class Evaluator extends Params {
   }
 
   /**
-   * Evaluates the output.
+   * Evaluates model output and returns a scalar metric.
+   * The value of [[isLargerBetter]] specifies whether larger values are better.
+   *
    * @param dataset a dataset that contains labels/observations and predictions.
    * @return metric
    */

mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala

@@ -78,9 +78,9 @@ private[feature] trait MinMaxScalerParams extends Params with HasInputCol with H
  * statistics, which is also known as min-max normalization or Rescaling. The rescaled value for
  * feature E is calculated as,
  *
- * Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
+ * `Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min`
  *
- * For the case E_{max} == E_{min}, Rescaled(e_i) = 0.5 * (max + min)
+ * For the case `E_{max} == E_{min}`, `Rescaled(e_i) = 0.5 * (max + min)`.
  * Note that since zero values will probably be transformed to non-zero values, output of the
  * transformer will be DenseVector even for sparse input.
  */

mllib/src/main/scala/org/apache/spark/ml/linalg/dataTypes.scala → mllib/src/main/scala/org/apache/spark/ml/linalg/SQLDataTypes.scala

@@ -17,15 +17,16 @@
 
 package org.apache.spark.ml.linalg
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.sql.types.DataType
 
 /**
  * :: DeveloperApi ::
  * SQL data types for vectors and matrices.
  */
+@Since("2.0.0")
 @DeveloperApi
-object sqlDataTypes {
+object SQLDataTypes {
 
   /** Data type for [[Vector]]. */
   val VectorType: DataType = new VectorUDT

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

@@ -376,7 +376,7 @@ object GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLine
     def deviance(y: Double, mu: Double, weight: Double): Double
 
     /**
-     * Akaike's 'An Information Criterion'(AIC) value of the family for a given dataset.
+     * Akaike Information Criterion (AIC) value of the family for a given dataset.
      *
      * @param predictions an RDD of (y, mu, weight) of instances in evaluation dataset
      * @param deviance the deviance for the fitted model in evaluation dataset
@@ -702,13 +702,13 @@ class GeneralizedLinearRegressionModel private[ml] (
 
   import GeneralizedLinearRegression._
 
-  lazy val familyObj = Family.fromName($(family))
-  lazy val linkObj = if (isDefined(link)) {
+  private lazy val familyObj = Family.fromName($(family))
+  private lazy val linkObj = if (isDefined(link)) {
     Link.fromName($(link))
   } else {
     familyObj.defaultLink
   }
-  lazy val familyAndLink = new FamilyAndLink(familyObj, linkObj)
+  private lazy val familyAndLink = new FamilyAndLink(familyObj, linkObj)
 
   override protected def predict(features: Vector): Double = {
     val eta = predictLink(features)
@@ -1021,7 +1021,7 @@ class GeneralizedLinearRegressionSummary private[regression] (
     rss / degreesOfFreedom
   }
 
-  /** Akaike's "An Information Criterion"(AIC) for the fitted model. */
+  /** Akaike Information Criterion (AIC) for the fitted model. */
   @Since("2.0.0")
   lazy val aic: Double = {
     val w = weightCol

mllib/src/test/java/org/apache/spark/ml/linalg/JavaSQLDataTypesSuite.java

@@ -20,7 +20,7 @@ package org.apache.spark.ml.linalg;
 import org.junit.Assert;
 import org.junit.Test;
 
-import static org.apache.spark.ml.linalg.sqlDataTypes.*;
+import static org.apache.spark.ml.linalg.SQLDataTypes.*;
 
 public class JavaSQLDataTypesSuite {
   @Test

mllib/src/test/scala/org/apache/spark/ml/linalg/SQLDataTypesSuite.scala

@@ -21,7 +21,7 @@ import org.apache.spark.SparkFunSuite
 
 class SQLDataTypesSuite extends SparkFunSuite {
   test("sqlDataTypes") {
-    assert(sqlDataTypes.VectorType === new VectorUDT)
-    assert(sqlDataTypes.MatrixType === new MatrixUDT)
+    assert(SQLDataTypes.VectorType === new VectorUDT)
+    assert(SQLDataTypes.MatrixType === new MatrixUDT)
   }
 }