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 9d8e4a90dbc99497921be3142c6fdbac911a683f..7fc371ec0f990f867979f0f8784d7b8acd99a0b2 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 5247c9c7486185a6bf26bb49cda45c26d0dc260d..e84a3a712df14f783a92bc47581d573f39a7dff8 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 5e51dbad760f465a277e6027a5b95c928bd68d44..de4474555d2d37d6647ed6b481d99bbedbcc17a9 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 ee811d3aa10152714e70f6b52c82b893fc92ba4b..a85aa2cac9e1be3b5f1ecfc0fb3d2c7f11addb25 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 b0144ef5331331349356c06e4a6ca2cc6cc8e30d..90e4687c1f4445fe24c915c05fce803a446053b3 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 f87611f5d4613356b4d64440c637a78110cdc00a..a70203028c85840b61399c312c52eff654901acb 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 669868787e8f0ca6a6ea73631288797b49a331ca..d39961809448709edb9f989d65fefd22d8c71016 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 632a2d537e5bc99703d4b19ec656726f80c808fb..31ba740ad4af0a007da907344787eda82359e7b5 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 4f925ede24d82569cb94151b2b19061e7c30122d..12394c867e973d978500c7701a530fdde27eea9b 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 f7a813695304fa4108c28adc745a966f4413f602..eb36697d94ba157c2c97ae7c7af4e1bab1581300 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 abeaaa00b5a4fe8dd154b2f1e7f071cf0e50ea8c..76cfb804e18f3ac608b291b819b663b07ae24968 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
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
index 390e9b6444c748b0114a9d2ee9017c5f442dc1ad..0b84e0a3fa7847bc94b4df1f3ed89ce11a84d033 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
@@ -82,8 +82,8 @@ class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") overrid
val metrics = new MulticlassMetrics(predictionAndLabels)
val metric = $(metricName) match {
case "f1" => metrics.weightedFMeasure
- case "precision" => metrics.precision
- case "recall" => metrics.recall
+ case "precision" => metrics.accuracy
+ case "recall" => metrics.accuracy
case "weightedPrecision" => metrics.weightedPrecision
case "weightedRecall" => metrics.weightedRecall
case "accuracy" => metrics.accuracy
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index ff1038cbf185f8b41dea550ae25701de97402ced..37552194c57d3d404c8486704311d1b75cad3c39 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -558,16 +558,18 @@ class LinearRegressionSummary private[regression] (
val predictionCol: String,
val labelCol: String,
val featuresCol: String,
- @deprecated("The model field is deprecated and will be removed in 2.1.0.", "2.0.0")
- val model: LinearRegressionModel,
+ private val privateModel: LinearRegressionModel,
private val diagInvAtWA: Array[Double]) extends Serializable {
+ @deprecated("The model field is deprecated and will be removed in 2.1.0.", "2.0.0")
+ val model: LinearRegressionModel = privateModel
+
@transient private val metrics = new RegressionMetrics(
predictions
.select(col(predictionCol), col(labelCol).cast(DoubleType))
.rdd
.map { case Row(pred: Double, label: Double) => (pred, label) },
- !model.getFitIntercept)
+ !privateModel.getFitIntercept)
/**
* Returns the explained variance regression score.
@@ -631,10 +633,10 @@ class LinearRegressionSummary private[regression] (
lazy val numInstances: Long = predictions.count()
/** Degrees of freedom */
- private val degreesOfFreedom: Long = if (model.getFitIntercept) {
- numInstances - model.coefficients.size - 1
+ private val degreesOfFreedom: Long = if (privateModel.getFitIntercept) {
+ numInstances - privateModel.coefficients.size - 1
} else {
- numInstances - model.coefficients.size
+ numInstances - privateModel.coefficients.size
}
/**
@@ -642,13 +644,15 @@ class LinearRegressionSummary private[regression] (
* the square root of the instance weights.
*/
lazy val devianceResiduals: Array[Double] = {
- val weighted = if (!model.isDefined(model.weightCol) || model.getWeightCol.isEmpty) {
- lit(1.0)
- } else {
- sqrt(col(model.getWeightCol))
- }
- val dr = predictions.select(col(model.getLabelCol).minus(col(model.getPredictionCol))
- .multiply(weighted).as("weightedResiduals"))
+ val weighted =
+ if (!privateModel.isDefined(privateModel.weightCol) || privateModel.getWeightCol.isEmpty) {
+ lit(1.0)
+ } else {
+ sqrt(col(privateModel.getWeightCol))
+ }
+ val dr = predictions
+ .select(col(privateModel.getLabelCol).minus(col(privateModel.getPredictionCol))
+ .multiply(weighted).as("weightedResiduals"))
.select(min(col("weightedResiduals")).as("min"), max(col("weightedResiduals")).as("max"))
.first()
Array(dr.getDouble(0), dr.getDouble(1))
@@ -668,14 +672,15 @@ class LinearRegressionSummary private[regression] (
throw new UnsupportedOperationException(
"No Std. Error of coefficients available for this LinearRegressionModel")
} else {
- val rss = if (!model.isDefined(model.weightCol) || model.getWeightCol.isEmpty) {
- meanSquaredError * numInstances
- } else {
- val t = udf { (pred: Double, label: Double, weight: Double) =>
- math.pow(label - pred, 2.0) * weight }
- predictions.select(t(col(model.getPredictionCol), col(model.getLabelCol),
- col(model.getWeightCol)).as("wse")).agg(sum(col("wse"))).first().getDouble(0)
- }
+ val rss =
+ if (!privateModel.isDefined(privateModel.weightCol) || privateModel.getWeightCol.isEmpty) {
+ meanSquaredError * numInstances
+ } else {
+ val t = udf { (pred: Double, label: Double, weight: Double) =>
+ math.pow(label - pred, 2.0) * weight }
+ predictions.select(t(col(privateModel.getPredictionCol), col(privateModel.getLabelCol),
+ col(privateModel.getWeightCol)).as("wse")).agg(sum(col("wse"))).first().getDouble(0)
+ }
val sigma2 = rss / degreesOfFreedom
diagInvAtWA.map(_ * sigma2).map(math.sqrt)
}
@@ -695,10 +700,10 @@ class LinearRegressionSummary private[regression] (
throw new UnsupportedOperationException(
"No t-statistic available for this LinearRegressionModel")
} else {
- val estimate = if (model.getFitIntercept) {
- Array.concat(model.coefficients.toArray, Array(model.intercept))
+ val estimate = if (privateModel.getFitIntercept) {
+ Array.concat(privateModel.coefficients.toArray, Array(privateModel.intercept))
} else {
- model.coefficients.toArray
+ privateModel.coefficients.toArray
}
estimate.zip(coefficientStandardErrors).map { x => x._1 / x._2 }
}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 90d382753131d6254e62a5829a823b736a4711c6..667290ece3514d2a6a1579a6cfc269570a97c6dd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -152,7 +152,7 @@ private[python] class PythonMLLibAPI extends Serializable {
intercept: Boolean,
validateData: Boolean,
convergenceTol: Double): JList[Object] = {
- val lrAlg = new LinearRegressionWithSGD()
+ val lrAlg = new LinearRegressionWithSGD(1.0, 100, 0.0, 1.0)
lrAlg.setIntercept(intercept)
.setValidateData(validateData)
lrAlg.optimizer
@@ -181,7 +181,7 @@ private[python] class PythonMLLibAPI extends Serializable {
intercept: Boolean,
validateData: Boolean,
convergenceTol: Double): JList[Object] = {
- val lassoAlg = new LassoWithSGD()
+ val lassoAlg = new LassoWithSGD(1.0, 100, 0.01, 1.0)
lassoAlg.setIntercept(intercept)
.setValidateData(validateData)
lassoAlg.optimizer
@@ -209,7 +209,7 @@ private[python] class PythonMLLibAPI extends Serializable {
intercept: Boolean,
validateData: Boolean,
convergenceTol: Double): JList[Object] = {
- val ridgeAlg = new RidgeRegressionWithSGD()
+ val ridgeAlg = new RidgeRegressionWithSGD(1.0, 100, 0.01, 1.0)
ridgeAlg.setIntercept(intercept)
.setValidateData(validateData)
ridgeAlg.optimizer
@@ -268,7 +268,7 @@ private[python] class PythonMLLibAPI extends Serializable {
intercept: Boolean,
validateData: Boolean,
convergenceTol: Double): JList[Object] = {
- val LogRegAlg = new LogisticRegressionWithSGD()
+ val LogRegAlg = new LogisticRegressionWithSGD(1.0, 100, 0.01, 1.0)
LogRegAlg.setIntercept(intercept)
.setValidateData(validateData)
LogRegAlg.optimizer
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index f3c52f61a3bb597bc1ac514649ce6d42b95b6746..adbcdd302aba9ef289c62d7c9b564a63ef9f3280 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -200,13 +200,12 @@ object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
/**
* Train a classification model for Binary Logistic Regression
* using Stochastic Gradient Descent. By default L2 regularization is used,
- * which can be changed via [[LogisticRegressionWithSGD.optimizer]].
+ * which can be changed via `LogisticRegressionWithSGD.optimizer`.
* NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1}
* for k classes multi-label classification problem.
* Using [[LogisticRegressionWithLBFGS]] is recommended over this.
*/
@Since("0.8.0")
-@deprecated("Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS", "2.0.0")
class LogisticRegressionWithSGD private[mllib] (
private var stepSize: Double,
private var numIterations: Int,
@@ -229,6 +228,7 @@ class LogisticRegressionWithSGD private[mllib] (
* numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.
*/
@Since("0.8.0")
+ @deprecated("Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS", "2.0.0")
def this() = this(1.0, 100, 0.01, 1.0)
override protected[mllib] def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index ef8c80f0cb8076e53b90a569d87818829d1a135b..e14bddf97d0f0993d8b76502ebf8624d382a1579 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -85,9 +85,7 @@ object LassoModel extends Loader[LassoModel] {
* See also the documentation for the precise formulation.
*/
@Since("0.8.0")
-@deprecated("Use ml.regression.LinearRegression with elasticNetParam = 1.0. Note the default " +
- "regParam is 0.01 for LassoWithSGD, but is 0.0 for LinearRegression.", "2.0.0")
-class LassoWithSGD private (
+class LassoWithSGD private[mllib] (
private var stepSize: Double,
private var numIterations: Int,
private var regParam: Double,
@@ -108,6 +106,8 @@ class LassoWithSGD private (
* regParam: 0.01, miniBatchFraction: 1.0}.
*/
@Since("0.8.0")
+ @deprecated("Use ml.regression.LinearRegression with elasticNetParam = 1.0. Note the default " +
+ "regParam is 0.01 for LassoWithSGD, but is 0.0 for LinearRegression.", "2.0.0")
def this() = this(1.0, 100, 0.01, 1.0)
override protected def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 9e9d98bc5e41ba64d914ce412f9ce88cbdca20b7..2ceac4b8cc319e5e2bee9fdcfd3c360326115fd6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -86,7 +86,6 @@ object LinearRegressionModel extends Loader[LinearRegressionModel] {
* See also the documentation for the precise formulation.
*/
@Since("0.8.0")
-@deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
class LinearRegressionWithSGD private[mllib] (
private var stepSize: Double,
private var numIterations: Int,
@@ -108,6 +107,7 @@ class LinearRegressionWithSGD private[mllib] (
* numIterations: 100, miniBatchFraction: 1.0}.
*/
@Since("0.8.0")
+ @deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
def this() = this(1.0, 100, 0.0, 1.0)
override protected[mllib] def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 512fb9a712b7aa4967c33b903c905ffc14422fa8..301f02fd98155d07b8f869a6e5262b9a4082d9de 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -86,9 +86,7 @@ object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
* See also the documentation for the precise formulation.
*/
@Since("0.8.0")
-@deprecated("Use ml.regression.LinearRegression with elasticNetParam = 0.0. Note the default " +
- "regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for LinearRegression.", "2.0.0")
-class RidgeRegressionWithSGD private (
+class RidgeRegressionWithSGD private[mllib] (
private var stepSize: Double,
private var numIterations: Int,
private var regParam: Double,
@@ -109,6 +107,8 @@ class RidgeRegressionWithSGD private (
* regParam: 0.01, miniBatchFraction: 1.0}.
*/
@Since("0.8.0")
+ @deprecated("Use ml.regression.LinearRegression with elasticNetParam = 0.0. Note the default " +
+ "regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for LinearRegression.", "2.0.0")
def this() = this(1.0, 100, 0.01, 1.0)
override protected def createModel(weights: Vector, intercept: Double) = {