Repository: spark
Updated Branches:
refs/heads/branch-2.1 ee735a8a8 -> 86b66216d
[SPARK-19110][ML][MLLIB] DistributedLDAModel returns different logPrior for
original and loaded model
## What changes were proposed in this pull request?
While adding DistributedLDAModel training summary for SparkR, I found that the
logPrior for original and loaded model is different.
For example, in the test("read/write DistributedLDAModel"), I add the test:
val logPrior = model.asInstanceOf[DistributedLDAModel].logPrior
val logPrior2 = model2.asInstanceOf[DistributedLDAModel].logPrior
assert(logPrior === logPrior2)
The test fails:
-4.394180878889078 did not equal -4.294290536919573
The reason is that `graph.vertices.aggregate(0.0)(seqOp, _ + _)` only returns
the value of a single vertex instead of the aggregation of all vertices.
Therefore, when the loaded model does the aggregation in a different order, it
returns different `logPrior`.
Please refer to #16464 for details.
## How was this patch tested?
Add a new unit test for testing logPrior.
Author: [email protected] <[email protected]>
Closes #16491 from wangmiao1981/ldabug.
(cherry picked from commit 036b50347c56a3541c526b1270093163b9b79e45)
Signed-off-by: Joseph K. Bradley <[email protected]>
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/86b66216
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/86b66216
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/86b66216
Branch: refs/heads/branch-2.1
Commit: 86b66216de411f8cbc79ede62b353f7cbb550903
Parents: ee735a8
Author: [email protected] <[email protected]>
Authored: Sat Jan 7 11:07:49 2017 -0800
Committer: Joseph K. Bradley <[email protected]>
Committed: Sat Jan 7 11:08:02 2017 -0800
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.../scala/org/apache/spark/mllib/clustering/LDAModel.scala | 4 ++--
.../test/scala/org/apache/spark/ml/clustering/LDASuite.scala | 8 ++++++++
2 files changed, 10 insertions(+), 2 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/86b66216/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
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diff --git
a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 25ffd85..b55f1b1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -745,12 +745,12 @@ class DistributedLDAModel private[clustering] (
val N_wk = vertex._2
val smoothed_N_wk: TopicCounts = N_wk + (eta - 1.0)
val phi_wk: TopicCounts = smoothed_N_wk :/ smoothed_N_k
- (eta - 1.0) * sum(phi_wk.map(math.log))
+ sumPrior + (eta - 1.0) * sum(phi_wk.map(math.log))
} else {
val N_kj = vertex._2
val smoothed_N_kj: TopicCounts = N_kj + (alpha - 1.0)
val theta_kj: TopicCounts = normalize(smoothed_N_kj, 1.0)
- (alpha - 1.0) * sum(theta_kj.map(math.log))
+ sumPrior + (alpha - 1.0) * sum(theta_kj.map(math.log))
}
}
graph.vertices.aggregate(0.0)(seqOp, _ + _)
http://git-wip-us.apache.org/repos/asf/spark/blob/86b66216/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
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diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
index 3f39ded..9aa11fb 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
@@ -260,6 +260,14 @@ class LDASuite extends SparkFunSuite with
MLlibTestSparkContext with DefaultRead
Vectors.dense(model2.topicsMatrix.toArray) absTol 1e-6)
assert(Vectors.dense(model.getDocConcentration) ~==
Vectors.dense(model2.getDocConcentration) absTol 1e-6)
+ val logPrior = model.asInstanceOf[DistributedLDAModel].logPrior
+ val logPrior2 = model2.asInstanceOf[DistributedLDAModel].logPrior
+ val trainingLogLikelihood =
+ model.asInstanceOf[DistributedLDAModel].trainingLogLikelihood
+ val trainingLogLikelihood2 =
+ model2.asInstanceOf[DistributedLDAModel].trainingLogLikelihood
+ assert(logPrior ~== logPrior2 absTol 1e-6)
+ assert(trainingLogLikelihood ~== trainingLogLikelihood2 absTol 1e-6)
}
val lda = new LDA()
testEstimatorAndModelReadWrite(lda, dataset,
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