diff options
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala | 5 | ||||
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala | 7 |
2 files changed, 12 insertions, 0 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index 6e4e6a6d85..cbcbfe8249 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -395,6 +395,10 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel] .map { row => Rating(row.getInt(0), row.getInt(1), row.getFloat(2)) } + val instrLog = Instrumentation.create(this, ratings) + instrLog.logParams(rank, numUserBlocks, numItemBlocks, implicitPrefs, alpha, + userCol, itemCol, ratingCol, predictionCol, maxIter, + regParam, nonnegative, checkpointInterval, seed) val (userFactors, itemFactors) = ALS.train(ratings, rank = $(rank), numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks), maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs), @@ -403,6 +407,7 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel] val userDF = userFactors.toDF("id", "features") val itemDF = itemFactors.toDF("id", "features") val model = new ALSModel(uid, $(rank), userDF, itemDF).setParent(this) + instrLog.logSuccess(model) copyValues(model) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala b/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala index 869104e090..71a626647a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala @@ -85,6 +85,13 @@ private[spark] class Instrumentation[E <: Estimator[_]] private ( } /** + * Logs the value with customized name field. + */ + def logNamedValue(name: String, num: Long): Unit = { + log(compact(render(name -> num))) + } + + /** * Logs the successful completion of the training session and the value of the learned model. */ def logSuccess(model: Model[_]): Unit = { |