[GitHub] spark issue #16441: [SPARK-14975][ML][WIP] Fixed GBTClassifier to predict pr...
Github user imatiach-msft commented on the issue: https://github.com/apache/spark/pull/16441 Indeed re-adding the constructor seems to make the binary compatibility tests pass (see spark QA build above). I think in favor of making the binary compat tests pass, we can keep the extra private constructor, even though for most people it won't do anything. Please let me know if there are any outstanding comments that still need to be addressed. Thank you! --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #16441: [SPARK-14975][ML][WIP] Fixed GBTClassifier to predict pr...
Github user imatiach-msft commented on the issue:
https://github.com/apache/spark/pull/16441
It looks like I am failing the binary compatibility tests despite this
constructor being private:
class GBTClassificationModel private[ml](
@Since("1.6.0") override val uid: String,
private val _trees: Array[DecisionTreeRegressionModel],
private val _treeWeights: Array[Double],
@Since("1.6.0") override val numFeatures: Int,
@Since("2.2.0") override val numClasses: Int)
extends ProbabilisticClassificationModel[Vector, GBTClassificationModel]
This is the same thing that happened in my original PR and then I had to
add the additional this() overload to pass the tests. In the PR comment it was
mentioned that I should be able to remove the unused constructor, does this
mean that I need to change the binary compatibility test somehow as well? My
guess is that the binary compat tests are java based and not scala based, in
which case private[ml] doesn't matter, so the solution would be to keep the
extra constructor I had before, just make sure that it is still private[ml],
only so I can pass the binary compat tests.
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[GitHub] spark issue #16441: [SPARK-14975][ML][WIP] Fixed GBTClassifier to predict pr...
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Github user imatiach-msft commented on the issue: https://github.com/apache/spark/pull/16441 @sethah @jkbradley thank you for the review - could you please take another look since I've updated the code review based on your comments? --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user imatiach-msft commented on the issue: https://github.com/apache/spark/pull/16441 @jkbradley I've updated based on your comments, please take another look, thanks! --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user imatiach-msft commented on the issue: https://github.com/apache/spark/pull/16441 Thanks, I've updated the PR based on your comment. The only disadvantage to the current code is that I do the probability computation within the classifier, but it seems like it should be moved to the LogLoss.scala class. However, it's not a problem right now because GBTClassifier only uses logistic loss, and other learners would have to be modified in a similar way as well probably. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user jkbradley commented on the issue: https://github.com/apache/spark/pull/16441 Thanks for the PR; I do want to get this fixed. However, I don't think this is the right way to make predictions of probabilities for GBTs. I believe it should depend on the loss used. E.g., check out page 8 of Friedman (1999) "Greedy Function Approximation? A Gradient Boosting Machine" --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #16441: [SPARK-14975][ML][WIP] Fixed GBTClassifier to predict pr...
Github user AmplabJenkins commented on the issue: https://github.com/apache/spark/pull/16441 Merged build finished. Test FAILed. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #16441: [SPARK-14975][ML][WIP] Fixed GBTClassifier to predict pr...
Github user SparkQA commented on the issue: https://github.com/apache/spark/pull/16441 **[Test build #70759 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/70759/testReport)** for PR 16441 at commit [`4468891`](https://github.com/apache/spark/commit/4468891cd83760a2f97ef257ef176a34bc79e5cd). --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
