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[SPARK-5987] [MLlib] Save/load for GaussianMixtureModels - #4986

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@MechCoder

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Should be self explanatory.

@MechCoder

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cc: @mengxr @jkbradley

@SparkQA

SparkQA commented Mar 11, 2015

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Test build #28481 has started for PR 4986 at commit 4898d57.

  • This patch merges cleanly.

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SparkQA commented Mar 11, 2015

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Test build #28481 has finished for PR 4986 at commit 4898d57.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds the following public classes (experimental):
    • case class Data(weights: Array[Double], mus: Array[Vector], sigmas: Array[Array[Double]])

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Test PASSed.
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Should be private.

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Agreed it should be private, but then it should be private in all other files as well.

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Test FAILed.
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@shaneknapp

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jenkins, test this please

@MechCoder

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@mengxr I am not sure if we should flatten it or not, would it be worth if the number of clusters is large? Also I think it would be better if we deal with MatrixUDT after this PR is done with. wdyt?

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SparkQA commented Mar 13, 2015

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Test build #28584 has started for PR 4986 at commit 9aaa535.

  • This patch merges cleanly.

@mengxr

mengxr commented Mar 13, 2015

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The number of clusters won't be very large. Flattening an Array[Array[Double]] doesn't copy the data, so there is no overhead. The content of parquet file is easy to inspect if we list each center as a record. I think we should just use Array[Double] instead being blocked by MatrixUDT. GMM models are usually dense.

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Test FAILed.
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jenkins, test this please

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SparkQA commented Mar 13, 2015

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Test build #28588 has started for PR 4986 at commit 9aaa535.

  • This patch merges cleanly.

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SparkQA commented Mar 13, 2015

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Test build #28588 has finished for PR 4986 at commit 9aaa535.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds the following public classes (experimental):
    • case class Data(weights: Array[Double], mus: Array[Vector], sigmas: Array[Array[Double]])

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Test PASSed.
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https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/28588/
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@MechCoder

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@mengxr I thing I have addressed your comments. sigmas is now stored as an Array of Doubles, Do you have any more comments? Thanks!

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SparkQA commented Mar 14, 2015

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Test build #28607 has started for PR 4986 at commit 4321743.

  • This patch merges cleanly.

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SparkQA commented Mar 14, 2015

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Test build #28607 has finished for PR 4986 at commit 4321743.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds the following public classes (experimental):
    • case class Data(weights: Array[Double], mus: Array[Vector], sigmas: Array[Double])

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Test PASSed.
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@MechCoder

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@mengxr I rebased over master and used MatrixUDT. Please review! :)

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SparkQA commented Mar 21, 2015

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Test build #28937 has started for PR 4986 at commit 23d707e.

  • This patch merges cleanly.

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This is not efficient because it may trigger multiple passes to the parquet file. Let's call collect() directly.

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@mengxr fixed !

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SparkQA commented Mar 24, 2015

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Test build #29101 has started for PR 4986 at commit e7a14cb.

  • This patch merges cleanly.

Comment thread docs/mllib-clustering.md

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Please also update the Java example.

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SparkQA commented Mar 24, 2015

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Test build #29101 has finished for PR 4986 at commit e7a14cb.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds the following public classes (experimental):
    • case class Data(weight: Double, mu: Vector, sigma: Matrix)

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Test PASSed.
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https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/29101/
Test PASSed.

@MechCoder

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@mengxr I have addressed your comments. Please have a look !

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SparkQA commented Mar 25, 2015

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Test build #29148 has started for PR 4986 at commit 7d2cd56.

  • This patch merges cleanly.

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SparkQA commented Mar 25, 2015

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Test build #29148 has finished for PR 4986 at commit 7d2cd56.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds the following public classes (experimental):
    • case class Data(weight: Double, mu: Vector, sigma: Matrix)

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Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/29148/
Test PASSed.

@mengxr

mengxr commented Mar 25, 2015

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LGTM. Merged into master. Thanks!!

@asfgit asfgit closed this in 4fc4d03 Mar 25, 2015
@MechCoder
MechCoder deleted the spark-5987 branch March 26, 2015 03:01
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@mengxr thanks for the merge! For supporting this in PySpark, we would need support for MatrixUDT, which would need support for sparse matrices right? I could not find any existing JIRA related to sparse matrix support, if you are able to please link me to it.

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6 participants