Repository: spark Updated Branches: refs/heads/branch-1.5 5cf266fde -> af98e51f2
[SPARK-10233] [MLLIB] update since version in mllib.evaluation Same as #8421 but for `mllib.evaluation`. cc avulanov Author: Xiangrui Meng <m...@databricks.com> Closes #8423 from mengxr/SPARK-10233. (cherry picked from commit 8668ead2e7097b9591069599fbfccf67c53db659) Signed-off-by: Xiangrui Meng <m...@databricks.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/af98e51f Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/af98e51f Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/af98e51f Branch: refs/heads/branch-1.5 Commit: af98e51f273d95e0fc19da1eca32a5f87a8c5576 Parents: 5cf266f Author: Xiangrui Meng <m...@databricks.com> Authored: Tue Aug 25 18:17:54 2015 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Tue Aug 25 18:18:27 2015 -0700 ---------------------------------------------------------------------- .../mllib/evaluation/BinaryClassificationMetrics.scala | 8 ++++---- .../spark/mllib/evaluation/MulticlassMetrics.scala | 11 ++++++++++- .../spark/mllib/evaluation/MultilabelMetrics.scala | 12 +++++++++++- .../spark/mllib/evaluation/RegressionMetrics.scala | 3 ++- 4 files changed, 27 insertions(+), 7 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/af98e51f/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala index 76ae847..508fe53 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala @@ -42,11 +42,11 @@ import org.apache.spark.sql.DataFrame * be smaller as a result, meaning there may be an extra sample at * partition boundaries. */ -@Since("1.3.0") +@Since("1.0.0") @Experimental -class BinaryClassificationMetrics( - val scoreAndLabels: RDD[(Double, Double)], - val numBins: Int) extends Logging { +class BinaryClassificationMetrics @Since("1.3.0") ( + @Since("1.3.0") val scoreAndLabels: RDD[(Double, Double)], + @Since("1.3.0") val numBins: Int) extends Logging { require(numBins >= 0, "numBins must be nonnegative") http://git-wip-us.apache.org/repos/asf/spark/blob/af98e51f/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala index 02e89d9..00e8376 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala @@ -33,7 +33,7 @@ import org.apache.spark.sql.DataFrame */ @Since("1.1.0") @Experimental -class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { +class MulticlassMetrics @Since("1.1.0") (predictionAndLabels: RDD[(Double, Double)]) { /** * An auxiliary constructor taking a DataFrame. @@ -140,6 +140,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { /** * Returns precision */ + @Since("1.1.0") lazy val precision: Double = tpByClass.values.sum.toDouble / labelCount /** @@ -148,23 +149,27 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { * because sum of all false positives is equal to sum * of all false negatives) */ + @Since("1.1.0") lazy val recall: Double = precision /** * Returns f-measure * (equals to precision and recall because precision equals recall) */ + @Since("1.1.0") lazy val fMeasure: Double = precision /** * Returns weighted true positive rate * (equals to precision, recall and f-measure) */ + @Since("1.1.0") lazy val weightedTruePositiveRate: Double = weightedRecall /** * Returns weighted false positive rate */ + @Since("1.1.0") lazy val weightedFalsePositiveRate: Double = labelCountByClass.map { case (category, count) => falsePositiveRate(category) * count.toDouble / labelCount }.sum @@ -173,6 +178,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { * Returns weighted averaged recall * (equals to precision, recall and f-measure) */ + @Since("1.1.0") lazy val weightedRecall: Double = labelCountByClass.map { case (category, count) => recall(category) * count.toDouble / labelCount }.sum @@ -180,6 +186,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { /** * Returns weighted averaged precision */ + @Since("1.1.0") lazy val weightedPrecision: Double = labelCountByClass.map { case (category, count) => precision(category) * count.toDouble / labelCount }.sum @@ -196,6 +203,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { /** * Returns weighted averaged f1-measure */ + @Since("1.1.0") lazy val weightedFMeasure: Double = labelCountByClass.map { case (category, count) => fMeasure(category, 1.0) * count.toDouble / labelCount }.sum @@ -203,5 +211,6 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { /** * Returns the sequence of labels in ascending order */ + @Since("1.1.0") lazy val labels: Array[Double] = tpByClass.keys.toArray.sorted } http://git-wip-us.apache.org/repos/asf/spark/blob/af98e51f/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala index a0a8d9c..c100b3c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala @@ -28,7 +28,7 @@ import org.apache.spark.sql.DataFrame * both are non-null Arrays, each with unique elements. */ @Since("1.2.0") -class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) { +class MultilabelMetrics @Since("1.2.0") (predictionAndLabels: RDD[(Array[Double], Array[Double])]) { /** * An auxiliary constructor taking a DataFrame. @@ -46,6 +46,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] * Returns subset accuracy * (for equal sets of labels) */ + @Since("1.2.0") lazy val subsetAccuracy: Double = predictionAndLabels.filter { case (predictions, labels) => predictions.deep == labels.deep }.count().toDouble / numDocs @@ -53,6 +54,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] /** * Returns accuracy */ + @Since("1.2.0") lazy val accuracy: Double = predictionAndLabels.map { case (predictions, labels) => labels.intersect(predictions).size.toDouble / (labels.size + predictions.size - labels.intersect(predictions).size)}.sum / numDocs @@ -61,6 +63,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] /** * Returns Hamming-loss */ + @Since("1.2.0") lazy val hammingLoss: Double = predictionAndLabels.map { case (predictions, labels) => labels.size + predictions.size - 2 * labels.intersect(predictions).size }.sum / (numDocs * numLabels) @@ -68,6 +71,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] /** * Returns document-based precision averaged by the number of documents */ + @Since("1.2.0") lazy val precision: Double = predictionAndLabels.map { case (predictions, labels) => if (predictions.size > 0) { predictions.intersect(labels).size.toDouble / predictions.size @@ -79,6 +83,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] /** * Returns document-based recall averaged by the number of documents */ + @Since("1.2.0") lazy val recall: Double = predictionAndLabels.map { case (predictions, labels) => labels.intersect(predictions).size.toDouble / labels.size }.sum / numDocs @@ -86,6 +91,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] /** * Returns document-based f1-measure averaged by the number of documents */ + @Since("1.2.0") lazy val f1Measure: Double = predictionAndLabels.map { case (predictions, labels) => 2.0 * predictions.intersect(labels).size / (predictions.size + labels.size) }.sum / numDocs @@ -143,6 +149,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] * Returns micro-averaged label-based precision * (equals to micro-averaged document-based precision) */ + @Since("1.2.0") lazy val microPrecision: Double = { val sumFp = fpPerClass.foldLeft(0L){ case(cum, (_, fp)) => cum + fp} sumTp.toDouble / (sumTp + sumFp) @@ -152,6 +159,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] * Returns micro-averaged label-based recall * (equals to micro-averaged document-based recall) */ + @Since("1.2.0") lazy val microRecall: Double = { val sumFn = fnPerClass.foldLeft(0.0){ case(cum, (_, fn)) => cum + fn} sumTp.toDouble / (sumTp + sumFn) @@ -161,10 +169,12 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])] * Returns micro-averaged label-based f1-measure * (equals to micro-averaged document-based f1-measure) */ + @Since("1.2.0") lazy val microF1Measure: Double = 2.0 * sumTp / (2 * sumTp + sumFnClass + sumFpClass) /** * Returns the sequence of labels in ascending order */ + @Since("1.2.0") lazy val labels: Array[Double] = tpPerClass.keys.toArray.sorted } http://git-wip-us.apache.org/repos/asf/spark/blob/af98e51f/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala index 36a6c35..799ebb9 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala @@ -32,7 +32,8 @@ import org.apache.spark.sql.DataFrame */ @Since("1.2.0") @Experimental -class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extends Logging { +class RegressionMetrics @Since("1.2.0") ( + predictionAndObservations: RDD[(Double, Double)]) extends Logging { /** * An auxiliary constructor taking a DataFrame. --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
