Github user rezazadeh commented on the pull request:
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Thanks for the review @mengxr !
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LGTM. Merged into master! Thanks @rezazadeh !
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Jenkins, test this please.
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@rezazadeh Could you set the exclusion rules in `dev/MimaExcludes.scala`?
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Only the binary compatibility test is failing, which is expected.
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Github user rezazadeh commented on the pull request:
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@mengxr I also added broadcasting of p and v to further optimize space
usage. Also now we're avoiding divide by zero if there is a column with zero
magnitude.
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@mengxr Merged in your changes and added ability for the threshold to be
larger with a warning. Tests pass.
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Github user mengxr commented on the pull request:
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@rezazadeh I sent a PR to your repo at:
https://github.com/rezazadeh/spark/pull/1 . Could you check the changes and
merge it if they are correct (hopefully) and look good
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@mengxr Thanks for the optimizations. I merged the latest master into my
branch and pushed to here. Would you like me to merge your branch into mine?
There is n
Github user mengxr commented on the pull request:
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@rezazadeh I made some changes in a local branch:
https://github.com/mengxr/spark/blob/rezazadeh-dimsumv2/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/Row
Github user debasish83 commented on the pull request:
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The code looks good for sparse input but for dense input is there any issue
with using activeIterator ? I understand that due to dimsum threshold check we
have to iter
Github user debasish83 commented on the pull request:
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ahh I saw it in the code now...that will do...no need for absolute
values...numbers are positive for me..thanks..
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Why do you say normL1 is not implemented? I have implemented normL1 in
MultivariateOnlineSummarizer, with tests. Do you want a version without
absolute values? If so, j
Github user debasish83 commented on the pull request:
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The colMags right now have sqrt(sum(column1)^2 + sum(column2)^2 + ... +
sum(columnN)^2)
It will be good to have (sum(column1) + sum(column2) + ... + sum(columnN))
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test this please
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@mengxr All requested changes made. All tests are passing locally. However,
I expect Jenkins to complain because of the new normL1 and normL2 methods added
to Multivari
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@rezazadeh I understand that it is easier to implement the algorithm on a
row-oriented format to compute similar columns. But it still sounds more
natural to me to compute
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@@ -390,6 +393,79 @@ class RowMatrix(
new RowMatrix(AB,
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1778#discussion_r17017214
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -390,6 +393,79 @@ class RowMatrix(
new RowMatrix(AB,
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1778#discussion_r17017206
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -390,6 +393,79 @@ class RowMatrix(
new RowMatrix(AB,
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1778#discussion_r17017210
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -390,6 +393,79 @@ class RowMatrix(
new RowMatrix(AB,
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1778#discussion_r17017216
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -390,6 +393,79 @@ class RowMatrix(
new RowMatrix(AB,
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1778#discussion_r17017204
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
---
@@ -27,10 +27,13 @@ import com.github.fommil.netlib.BLAS.{getInst
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/1778#issuecomment-53976440
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Github user rezazadeh commented on the pull request:
https://github.com/apache/spark/pull/1778#issuecomment-53975250
Style changes made. Experimental results below.
We run DIMSUM daily on a production-scale ads dataset. After replacing the
traditional cosine similarity computa
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/1778#issuecomment-53943844
@rezazadeh Could you update the PR to follow [Spark Code Style
Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide)?
Thanks!
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