reverted convergenceTol to tol

Author: jkbradley

mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala

@@ -265,18 +265,18 @@ private[ml] trait HasElasticNetParam extends Params {
 }
 
 /**
- * (private[ml]) Trait for shared param convergenceTol.
+ * (private[ml]) Trait for shared param tol.
  */
-private[ml] trait HasConvergenceTol extends Params {
+private[ml] trait HasTol extends Params {
 
   /**
    * Param for the convergence tolerance for iterative algorithms.
    * @group param
    */
-  final val convergenceTol: DoubleParam = new DoubleParam(this, "convergenceTol", "the convergence tolerance for iterative algorithms")
+  final val tol: DoubleParam = new DoubleParam(this, "tol", "the convergence tolerance for iterative algorithms")
 
   /** @group getParam */
-  final def getConvergenceTol: Double = getOrDefault(convergenceTol)
+  final def getTol: Double = getOrDefault(tol)
 }
 
 /**

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

@@ -25,7 +25,7 @@ import breeze.optimize.{CachedDiffFunction, DiffFunction}
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param.{Params, ParamMap}
-import org.apache.spark.ml.param.shared.{HasConvergenceTol, HasElasticNetParam, HasMaxIter,
+import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter,
   HasRegParam}
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -41,7 +41,7 @@ import org.apache.spark.Logging
  * Params for linear regression.
  */
 private[regression] trait LinearRegressionParams extends RegressorParams
-  with HasRegParam with HasElasticNetParam with HasMaxIter with HasConvergenceTol
+  with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
 
 /**
  * :: AlphaComponent ::
@@ -94,8 +94,8 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
    * Default is 1E-6.
    * @group setParam
    */
-  def setTol(value: Double): this.type = set(convergenceTol, value)
-  setDefault(convergenceTol -> 1E-6)
+  def setTol(value: Double): this.type = set(tol, value)
+  setDefault(tol -> 1E-6)
 
   override protected def train(dataset: DataFrame, paramMap: ParamMap): LinearRegressionModel = {
     // Extract columns from data.  If dataset is persisted, do not persist instances.
@@ -144,10 +144,10 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
       featuresStd, featuresMean, effectiveL2RegParam)
 
     val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
-      new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(convergenceTol))
+      new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol))
     } else {
       new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam,
-        paramMap(convergenceTol))
+        paramMap(tol))
     }
 
     val initialWeights = Vectors.zeros(numFeatures)