[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
Github user hhbyyh commented on the issue: https://github.com/apache/spark/pull/19020 LGTM. One thing I noticed is that we did not really compare the loss with other lib (like sklearn), which is something also missing for other linear algorithms. Do you think it would be a good idea to add it? --- - 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 WeichenXu123 commented on the issue: https://github.com/apache/spark/pull/19020 LGTM. thanks! --- - 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 yanboliang commented on the issue: https://github.com/apache/spark/pull/19020 @hhbyyh I have compared this implementation with sklearn ```HuberRegressor``` on several dataset listed at https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html, they can produce consistent result. Thanks. --- - 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 WeichenXu123 commented on the issue: https://github.com/apache/spark/pull/19020 I also vote to combine them as one estimator, here are my two cents: 1, Regression with Huber loss is one kind of linear regression. It makes sense to switch between different loss functions. 2, To combine them as one estimator should be more visible to users. Users should be easy to try linear regression with different loss function. 3, It will reduce lots of code duplication. thanks! --- - 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/19020 > We have two candidate name: epsilon or m I see; that seems fine then, though I worry that we use "epsilon" in MLlib (tests) for "a very small positive number." Can we document it more clearly, including the comment that it matches sklearn and is "M" from the paper? > provide the estimated scaling factor (sigma from the paper) I'd say: * Either we provide it as 1 for regular linear regression (since that is technically correct) * Or we take this as indication that @sethah 's comment about separating the classes is better. Re: @sethah 's comment about separating classes, I'll comment in the JIRA since that's a bigger discussion. --- - 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 #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
Github user sethah commented on the issue: https://github.com/apache/spark/pull/19020 @yanboliang Yeah, I saw the discussion and it seems to me the reason was: there would be too much code duplication. Sure, it's true that there would be code duplication, but to me that's a reason to work on the internals so that there is less code duplication, rather than just to continue patching around a design that doesn't work very well. We *can* combine them, I just don't think we should. I know I'm late to the discussion, so there's already been a lot of work. But these things can't really be undone due to backwards compatibility. We could work on creating better interfaces for plugging in loss/prediction/optimizer, which I think is the best way to approach it. Linear and logistic regression seem like they are just becoming giant, monolithic pieces of code. I guess the argument against it will be lack of developer bandwidth. If that's the case, ok, but I'd argue to just leave Huber regression to be implemented by an external package in that case. If we don't have bandwidth do it in a robust, well-designed way then I don't think doing it the *easy* way is a good solution either. My first vote is to implement as a separate estimator, my second vote would be to leave it for a Spark package. --- - 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 #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
Github user yanboliang commented on the issue: https://github.com/apache/spark/pull/19020 @sethah To the issue that whether huber linear regression share codebase with ```LinearRegression```, we have discussion at [JIRA](https://issues.apache.org/jira/browse/SPARK-3181). At last @dbtsai and I reached an agreement to combine them in a single class. Actually in sklearn, there are separate [```HuberRegressor```](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html) and [```SGDRegressor```](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html) support to train linear regression with huber loss. Also, in this [paper](http://statweb.stanford.edu/~owen/reports/hhu.pdf), huber regression is a robust hybrid of lasso and ridge regression, so I think we can combine them together. @jkbradley @MLnick @WeichenXu123 @hhbyyh What's your opinion? Thanks. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
Github user yanboliang commented on the issue: https://github.com/apache/spark/pull/19020 @jkbradley Thanks for your comments, I have addressed all your inline comments. Please see replies to your other questions below: > Echoing @WeichenXu123 's comment: Why use "epsilon" as the Param name? We have two candidate name: _epsilon_ or _m_ , both of them are not very descriptive. I referred sklearn [HuberRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html), and keep consistent with it. > I'd like us to provide the estimated scaling factor (sigma from the paper) in the Model. That seems useful for model interpretation and debugging. I'm hesitating to add it to ```LinearRegression``` in case to confuse users who just try with different losses, but I'm OK to add it. Which should be output for _sigma_ if users fit with _squaredError_? A default value or throwing exception? Thanks. --- - 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 sethah commented on the issue: https://github.com/apache/spark/pull/19020 I disagree that this should be combined with Linear Regression. IMO, this belongs as its own algorithm. The fact that there would be code duplication in that case is indicative that we don't have good abstractions and code sharing in place, not that we should combine different algorithms using `case` expressions internally. --- - 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/19020 I'll check this out now --- - 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 felixcheung commented on the issue: https://github.com/apache/spark/pull/19020 big vote for python and R :) --- - 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 WeichenXu123 commented on the issue: https://github.com/apache/spark/pull/19020 Looks good. cc @jkbradley Thanks! --- - 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 yanboliang commented on the issue: https://github.com/apache/spark/pull/19020 @MLnick @WeichenXu123 Thanks for your comments, also cc @jkbradley @hhbyyh @sethah, would you mind to have a 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 yanboliang commented on the issue: https://github.com/apache/spark/pull/19020 @MLnick Yeah, I think we have get an agreement in JIRA discussion. --- 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 MLnick commented on the issue: https://github.com/apache/spark/pull/19020 So did we decide not to expose the equivalents of `scale_` and `outliers_` from sklearn? --- 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 #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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[GitHub] spark issue #19020: [SPARK-3181] [ML] Implement huber loss for LinearRegress...
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