Repository: spark Updated Branches: refs/heads/master 0f61d6efb -> b0a03feef
[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 <so...@cloudera.com> Author: Joseph K. Bradley <jos...@databricks.com> Closes #13314 from jkbradley/warning-cleanups. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/b0a03fee Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/b0a03fee Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/b0a03fee Branch: refs/heads/master Commit: b0a03feef2cf4daa7642ec7f4dc479dbd473b581 Parents: 0f61d6e Author: Sean Owen <so...@cloudera.com> Authored: Thu May 26 14:25:28 2016 -0700 Committer: Joseph K. Bradley <jos...@databricks.com> Committed: Thu May 26 14:25:28 2016 -0700 ---------------------------------------------------------------------- .../JavaLogisticRegressionWithLBFGSExample.java | 4 +- ...aMulticlassClassificationMetricsExample.java | 4 +- .../spark/examples/ml/DecisionTreeExample.scala | 2 +- .../examples/mllib/DecisionTreeRunner.scala | 10 ++-- .../mllib/GradientBoostedTreesRunner.scala | 5 +- .../spark/examples/mllib/LinearRegression.scala | 1 + .../mllib/LinearRegressionWithSGDExample.scala | 1 + .../LogisticRegressionWithLBFGSExample.scala | 4 +- .../mllib/MulticlassMetricsExample.scala | 8 +-- .../spark/examples/mllib/PCAExample.scala | 1 + .../mllib/RegressionMetricsExample.scala | 2 + .../MulticlassClassificationEvaluator.scala | 4 +- .../spark/ml/regression/LinearRegression.scala | 53 +++++++++++--------- .../spark/mllib/api/python/PythonMLLibAPI.scala | 8 +-- .../classification/LogisticRegression.scala | 4 +- .../apache/spark/mllib/regression/Lasso.scala | 6 +-- .../mllib/regression/LinearRegression.scala | 2 +- .../mllib/regression/RidgeRegression.scala | 6 +-- 18 files changed, 63 insertions(+), 62 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java ---------------------------------------------------------------------- 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 9d8e4a9..7fc371e 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"); http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java ---------------------------------------------------------------------- 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 5247c9c..e84a3a7 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++) { http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala ---------------------------------------------------------------------- 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 5e51dba..de44745 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") } http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala ---------------------------------------------------------------------- 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 ee811d3..a85aa2c 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) { http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala ---------------------------------------------------------------------- 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 b0144ef..90e4687 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() http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala ---------------------------------------------------------------------- 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 f87611f..a702030 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 { http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegressionWithSGDExample.scala ---------------------------------------------------------------------- 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 6698687..d399618 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 = { http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala ---------------------------------------------------------------------- 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 632a2d5..31ba740 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") http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/mllib/MulticlassMetricsExample.scala ---------------------------------------------------------------------- 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 4f925ed..12394c8 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 http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/mllib/PCAExample.scala ---------------------------------------------------------------------- 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 f7a8136..eb36697 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 = { http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala ---------------------------------------------------------------------- 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 abeaaa0..76cfb80 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 http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala ---------------------------------------------------------------------- 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 390e9b6..0b84e0a 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 http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---------------------------------------------------------------------- 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 ff1038c..3755219 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 } } http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala ---------------------------------------------------------------------- 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 90d3827..667290e 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 http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala ---------------------------------------------------------------------- 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 f3c52f6..adbcdd3 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) = { http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala ---------------------------------------------------------------------- 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 ef8c80f..e14bddf 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) = { http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala ---------------------------------------------------------------------- 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 9e9d98b..2ceac4b 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) = { http://git-wip-us.apache.org/repos/asf/spark/blob/b0a03fee/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala ---------------------------------------------------------------------- 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 512fb9a..301f02f 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) = { --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
