diff options
4 files changed, 7 insertions, 7 deletions
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 8fc9860566..89718e0f3e 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 @@ -355,9 +355,9 @@ class LinearRegressionSummary private[regression] ( */ val r2: Double = metrics.r2 - /** Residuals (predicted value - label value) */ + /** Residuals (label - predicted value) */ @transient lazy val residuals: DataFrame = { - val t = udf { (pred: Double, label: Double) => pred - label} + val t = udf { (pred: Double, label: Double) => label - pred } predictions.select(t(col(predictionCol), col(labelCol)).as("residuals")) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala index a5582d3ef3..011a5d5742 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala @@ -42,11 +42,11 @@ object SquaredError extends Loss { * @return Loss gradient */ override def gradient(prediction: Double, label: Double): Double = { - 2.0 * (prediction - label) + - 2.0 * (label - prediction) } override private[mllib] def computeError(prediction: Double, label: Double): Double = { - val err = prediction - label + val err = label - prediction err * err } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala index cf120cf2a4..374002c5b4 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala @@ -302,7 +302,7 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { .map { case Row(features: DenseVector, label: Double) => val prediction = features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept - prediction - label + label - prediction } .zip(model.summary.residuals.map(_.getDouble(0))) .collect() @@ -314,7 +314,7 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { Use the following R code to generate model training results. predictions <- predict(fit, newx=features) - residuals <- predictions - label + residuals <- label - predictions > mean(residuals^2) # MSE [1] 0.009720325 > mean(abs(residuals)) # MAD diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/EnsembleTestHelper.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/EnsembleTestHelper.scala index 8972c229b7..334bf3790f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/EnsembleTestHelper.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/EnsembleTestHelper.scala @@ -70,7 +70,7 @@ object EnsembleTestHelper { metricName: String = "mse") { val predictions = input.map(x => model.predict(x.features)) val errors = predictions.zip(input.map(_.label)).map { case (prediction, label) => - prediction - label + label - prediction } val metric = metricName match { case "mse" => |