[MINOR][ML] Fix naming conventions of AFTSurvivalRegression coefficients

Rename ```regressionCoefficients``` back to ```coefficients```, and name ```weights``` to ```parameters```.
See discussion [here](https://github.com/apache/spark/pull/9311/files#diff-e277fd0bc21f825d3196b4551c01fe5fR230). mengxr vectorijk dbtsai

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9431 from yanboliang/aft-coefficients.

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

@@ -200,17 +200,17 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
 
     val numFeatures = dataset.select($(featuresCol)).take(1)(0).getAs[Vector](0).size
     /*
-       The coefficients vector has three parts:
+       The parameters vector has three parts:
        the first element: Double, log(sigma), the log of scale parameter
        the second element: Double, intercept of the beta parameter
        the third to the end elements: Doubles, regression coefficients vector of the beta parameter
      */
-    val initialCoefficients = Vectors.zeros(numFeatures + 2)
+    val initialParameters = Vectors.zeros(numFeatures + 2)
 
     val states = optimizer.iterations(new CachedDiffFunction(costFun),
-      initialCoefficients.toBreeze.toDenseVector)
+      initialParameters.toBreeze.toDenseVector)
 
-    val coefficients = {
+    val parameters = {
       val arrayBuilder = mutable.ArrayBuilder.make[Double]
       var state: optimizer.State = null
       while (states.hasNext) {
@@ -227,10 +227,10 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
 
     if (handlePersistence) instances.unpersist()
 
-    val regressionCoefficients = Vectors.dense(coefficients.slice(2, coefficients.length))
-    val intercept = coefficients(1)
-    val scale = math.exp(coefficients(0))
-    val model = new AFTSurvivalRegressionModel(uid, regressionCoefficients, intercept, scale)
+    val coefficients = Vectors.dense(parameters.slice(2, parameters.length))
+    val intercept = parameters(1)
+    val scale = math.exp(parameters(0))
+    val model = new AFTSurvivalRegressionModel(uid, coefficients, intercept, scale)
     copyValues(model.setParent(this))
   }
 
@@ -251,7 +251,7 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
 @Since("1.6.0")
 class AFTSurvivalRegressionModel private[ml] (
     @Since("1.6.0") override val uid: String,
-    @Since("1.6.0") val regressionCoefficients: Vector,
+    @Since("1.6.0") val coefficients: Vector,
     @Since("1.6.0") val intercept: Double,
     @Since("1.6.0") val scale: Double)
   extends Model[AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams {
@@ -275,7 +275,7 @@ class AFTSurvivalRegressionModel private[ml] (
   @Since("1.6.0")
   def predictQuantiles(features: Vector): Vector = {
     // scale parameter for the Weibull distribution of lifetime
-    val lambda = math.exp(BLAS.dot(regressionCoefficients, features) + intercept)
+    val lambda = math.exp(BLAS.dot(coefficients, features) + intercept)
     // shape parameter for the Weibull distribution of lifetime
     val k = 1 / scale
     val quantiles = $(quantileProbabilities).map {
@@ -286,7 +286,7 @@ class AFTSurvivalRegressionModel private[ml] (
 
   @Since("1.6.0")
   def predict(features: Vector): Double = {
-    math.exp(BLAS.dot(regressionCoefficients, features) + intercept)
+    math.exp(BLAS.dot(coefficients, features) + intercept)
   }
 
   @Since("1.6.0")
@@ -309,7 +309,7 @@ class AFTSurvivalRegressionModel private[ml] (
 
   @Since("1.6.0")
   override def copy(extra: ParamMap): AFTSurvivalRegressionModel = {
-    copyValues(new AFTSurvivalRegressionModel(uid, regressionCoefficients, intercept, scale), extra)
+    copyValues(new AFTSurvivalRegressionModel(uid, coefficients, intercept, scale), extra)
       .setParent(parent)
   }
 }
@@ -369,17 +369,17 @@ class AFTSurvivalRegressionModel private[ml] (
  *   \frac{\partial (-\iota)}{\partial (\log\sigma)}=
  *   \sum_{i=1}^{n}[\delta_{i}+(\delta_{i}-e^{\epsilon_{i}})\epsilon_{i}]
  * }}}
- * @param coefficients including three part: The log of scale parameter, the intercept and
+ * @param parameters including three part: The log of scale parameter, the intercept and
  *                regression coefficients corresponding to the features.
  * @param fitIntercept Whether to fit an intercept term.
  */
-private class AFTAggregator(coefficients: BDV[Double], fitIntercept: Boolean)
+private class AFTAggregator(parameters: BDV[Double], fitIntercept: Boolean)
   extends Serializable {
 
   // beta is the intercept and regression coefficients to the covariates
-  private val beta = coefficients.slice(1, coefficients.length)
+  private val beta = parameters.slice(1, parameters.length)
   // sigma is the scale parameter of the AFT model
-  private val sigma = math.exp(coefficients(0))
+  private val sigma = math.exp(parameters(0))
 
   private var totalCnt: Long = 0L
   private var lossSum = 0.0
@@ -449,15 +449,15 @@ private class AFTAggregator(coefficients: BDV[Double], fitIntercept: Boolean)
 
 /**
  * AFTCostFun implements Breeze's DiffFunction[T] for AFT cost.
- * It returns the loss and gradient at a particular point (coefficients).
+ * It returns the loss and gradient at a particular point (parameters).
  * It's used in Breeze's convex optimization routines.
  */
 private class AFTCostFun(data: RDD[AFTPoint], fitIntercept: Boolean)
   extends DiffFunction[BDV[Double]] {
 
-  override def calculate(coefficients: BDV[Double]): (Double, BDV[Double]) = {
+  override def calculate(parameters: BDV[Double]): (Double, BDV[Double]) = {
 
-    val aftAggregator = data.treeAggregate(new AFTAggregator(coefficients, fitIntercept))(
+    val aftAggregator = data.treeAggregate(new AFTAggregator(parameters, fitIntercept))(
       seqOp = (c, v) => (c, v) match {
         case (aggregator, instance) => aggregator.add(instance)
       },

mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala

@@ -141,12 +141,12 @@ class AFTSurvivalRegressionSuite extends SparkFunSuite with MLlibTestSparkContex
        Number of Newton-Raphson Iterations: 5
        n= 1000
      */
-    val regressionCoefficientsR = Vectors.dense(-0.039)
+    val coefficientsR = Vectors.dense(-0.039)
     val interceptR = 1.759
     val scaleR = 1.41
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.regressionCoefficients ~== regressionCoefficientsR relTol 1E-3)
+    assert(model.coefficients ~== coefficientsR relTol 1E-3)
     assert(model.scale ~== scaleR relTol 1E-3)
 
     /*
@@ -212,12 +212,12 @@ class AFTSurvivalRegressionSuite extends SparkFunSuite with MLlibTestSparkContex
        Number of Newton-Raphson Iterations: 5
        n= 1000
      */
-    val regressionCoefficientsR = Vectors.dense(-0.0844, 0.0677)
+    val coefficientsR = Vectors.dense(-0.0844, 0.0677)
     val interceptR = 1.9206
     val scaleR = 0.977
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.regressionCoefficients ~== regressionCoefficientsR relTol 1E-3)
+    assert(model.coefficients ~== coefficientsR relTol 1E-3)
     assert(model.scale ~== scaleR relTol 1E-3)
 
     /*
@@ -282,12 +282,12 @@ class AFTSurvivalRegressionSuite extends SparkFunSuite with MLlibTestSparkContex
        Number of Newton-Raphson Iterations: 6
        n= 1000
      */
-    val regressionCoefficientsR = Vectors.dense(0.896, -0.709)
+    val coefficientsR = Vectors.dense(0.896, -0.709)
     val interceptR = 0.0
     val scaleR = 1.52
 
     assert(model.intercept === interceptR)
-    assert(model.regressionCoefficients ~== regressionCoefficientsR relTol 1E-3)
+    assert(model.coefficients ~== coefficientsR relTol 1E-3)
     assert(model.scale ~== scaleR relTol 1E-3)
 
     /*