Github user freeman-lab commented on a diff in the pull request:
https://github.com/apache/spark/pull/4306#discussion_r24038692
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mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
---
@@ -58,14 +58,14 @@ abstract class
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4306#issuecomment-72530762
LGTM except minor inline comments.
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Github user SparkQA commented on the pull request:
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Github user freeman-lab commented on the pull request:
https://github.com/apache/spark/pull/4306#issuecomment-72555492
Thanks for the review! I think I dealt with everything, and in a couple
places I tweaked the corresponding point in `StreamingLinearRegression` for
parity.
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Github user freeman-lab commented on a diff in the pull request:
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala
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+/*
+ * Licensed to the
Github user mengxr commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
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Github user mengxr commented on a diff in the pull request:
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Github user freeman-lab commented on a diff in the pull request:
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+/*
+ *
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Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4306#issuecomment-72572634
test this please
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Github user freeman-lab commented on the pull request:
https://github.com/apache/spark/pull/4306#issuecomment-72583404
Should we just set `initialWeights=Vectors.dense(0.0)` by default?
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Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4306#issuecomment-72586638
Could we try to set model type to `Option[Model]` and check whether it is
set in the code?
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Github user SparkQA commented on the pull request:
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Github user freeman-lab commented on the pull request:
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@mengxr the test failure is real, I think there's a conflict due to this
recent change to master
Github user freeman-lab commented on the pull request:
https://github.com/apache/spark/pull/4306#issuecomment-72588966
Nice idea! Just took a stab, see what you think. Was reproducing the test
failure locally, and it is now fixed with this change.
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Github user mengxr commented on a diff in the pull request:
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Github user freeman-lab commented on the pull request:
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Ok, the problem is that we've been setting `initialWeights` to `null` by
default and then checking that weights are defined before starting training
(because we
Github user asfgit closed the pull request at:
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Github user mengxr commented on the pull request:
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Merged into master. Thanks!!
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Github user mengxr commented on a diff in the pull request:
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala
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@@ -0,0 +1,74 @@
+/*
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Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4306#discussion_r23941945
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
---
@@ -0,0 +1,97 @@
+/*
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Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4306#discussion_r23941941
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
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Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4306#discussion_r23941944
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
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Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4306#discussion_r23941932
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLogisticRegression.scala
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Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4306#discussion_r23942029
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
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@@ -0,0 +1,97 @@
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GitHub user freeman-lab opened a pull request:
https://github.com/apache/spark/pull/4306
[SPARK-4979][MLLIB] Streaming logisitic regression
This adds support for streaming logistic regression with stochastic
gradient descent, in the same manner as the existing implementation of
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