[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15675699#comment-15675699 ] German Eduardo Melo commented on SPARK-9478: [~sethah] I was wondering if you are working on this request...the current PR for the improvement is https://github.com/apache/spark/pull/13851, right? Due my research I am looking forward this feature, thanks a lot in advance for any update! > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15626725#comment-15626725 ] Joseph K. Bradley commented on SPARK-9478: -- Removing target version since 2.1 RC1 is being cut soon > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15563919#comment-15563919 ] Seth Hendrickson commented on SPARK-9478: - I'm going to revive this, and hopefully submit a PR soon. > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[
https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15347138#comment-15347138
]
Seth Hendrickson commented on SPARK-9478:
-
[~mengxr] Thanks for your feedback. Originally I did not implement a change to
the sampling semantics, though after some thought it does not seem entirely
correct to only apply the sampling weights after bagging. I checked
scikit-learn and they do not use weighted sampling (instead applying weights
after taking uniform samples), but I think we should implement the weighted
sampling assuming it can fit into the current Spark abstractions.
>From my understanding, it is reasonable to use the Poisson distribution as an
>approximation to the Multinomial sampling. Currently, we approximate binomial
>sampling using a Poisson sampler with constant mean. To implement weighted
>sampling with replacement, we can use a Poisson sampler with mean parameter
>proportional to the sample weight - is that correct? We could use the
>{{RandomDataGenerator}} class in StratifiedSamplingUtils, which maintains a
>cache of Poisson sampling functions. I am not an expert in sampling algorithms
>so I really appreciate your thoughts on this.
> Add class weights to Random Forest
> --
>
> Key: SPARK-9478
> URL: https://issues.apache.org/jira/browse/SPARK-9478
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
>Affects Versions: 1.4.1
>Reporter: Patrick Crenshaw
>
> Currently, this implementation of random forest does not support class
> weights. Class weights are important when there is imbalanced training data
> or the evaluation metric of a classifier is imbalanced (e.g. true positive
> rate at some false positive threshold).
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[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15345946#comment-15345946 ] Xiangrui Meng commented on SPARK-9478: -- Sorry for being late in the discussion! Instance weight support is a superset of class weight support since we can easily map instance labels to corresponding weights as the instance weights. Is it correct? There could be storage overhead since it is hard to tell inside the algorithm whether the column is mapped or has raw data. We can add an extra counter to the aggregator to keep the semantic of "minInstancesPerNode". Neither of the issues would prevent us implementing instance weights. So I would +1 on implementing instance weight, which also covers class weight. It also helps the use cases where the labeled instances are associated with confidence scores, e.g., labels from implicit observations. I didn't find any discussion about the semantic change in sampling strategy. No matter we implement class weights or instance weights, the simple random sampling should become weighted sampling to reflect weights, and the weights should be updated based on the sample ratio. Otherwise, even we put large weights on few instances with rare labels, they are likely to be dropped during sampling. Just my two cents, I think [~josephkb] could help make a decision. > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15345447#comment-15345447 ] Yuewei Na commented on SPARK-9478: -- Hi [~sethah]. Actually, the code I PR has been used in our company for a period of time and we recently decide to make it open sourced. We used this implementation due to the fact that there is no class weights support in the current version and we do have practical needs. Comparing to sample weights, our version saves more memory since ours don't need to add a column to store sample weights. At the same time, I browsed the APIs and implementations of the ensemble methods in scikit-learn. It's true that the class weights are integrated together with sample weights there. Together with the need of sample weights in other various models, I agree that a functionality that supports sample weights is a better choice. So now I have some thoughts on this problem: 1. I agree with you on implementing a mechanism to support class weights. I think it will reduce users' effort to achieve their goal. 2. Since my PR is a lightweight version and it has been tested and used in our company for a period of time, we could review and merge my PR to the master branch first to make it available to users who need it. And we can remove it when there are no problems that block the instance weight version while preserving the same interface for setting the class weights. We could either create a new JIRA which separates the problem 'adding class weights' and the problem 'adding instance weights'. But at least, the title of the current JIRA should be changed or a new JIRA should be created. > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15345208#comment-15345208 ] Seth Hendrickson commented on SPARK-9478: - Thanks for your timely feedback! There are many use cases for sample weights in machine learning algorithms that are broadly applicable. In regression, it is common to use sample weights to account for changing variance in the data generation process. Sample weights can also be used in both classification and regression to weight more recent data points that may be more reflective of the data generation model. Handling imbalanced datasets with class weights can be seen as a specific case of sample weights. Using upsampling/downsampling can cause unnecessary duplication of the input data and also makes it more difficult to assign arbitrary weights to samples. Even further, implementing weighted boosting algorithms like AdaBoost/LogitBoost etc... will not be possible without sample weights. Scikit-learn does indeed support sample weights, as you can see [here|http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.fit], and in fact the algorithms simply convert class weights into sample weights. With this in mind, I think we should support sample weights. We might also want to implement a mechanism to support class weights in the API where users don't have to manually convert class weights to sample weights - we can open a new JIRA to discuss it. [There is an ongoing effort in MLlib to support instance weighting|https://issues.apache.org/jira/browse/SPARK-9610] in the various algorithms and so I think it is beneficial to add it to trees and forests. > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15345105#comment-15345105 ] Yuewei Na commented on SPARK-9478: -- Hi [~sethah], thanks a lot for your comment on my PR and your continual concerns on this problem. Sorry for not commenting before I made this PR. Like what you said, the major reason for me to make another PR is exactly because of the title of this JIRA. I implement this class weight version instead of sticking to instance weight because: 1. Existing implementations in other languages or packages, e.g. rpart in R and sklearn in Python all support class weights instead of instance weights. Indeed, instance weights make weighting in regression also possible. But the major application in handling imbalanced dataset is classification. If one does need such feature, it could be done by downsampling or upsampling the whole dataset. For the materials that I've read, including the book 'Elements of Statistical Learning', Rpart's documentation(https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf) and some professor's PPT. I've never seen the use cases for handling imbalanced dataset in regression problems using Random Forest. I would be very happy if someone could tell me when it's needed. 2. As you commented in the first PR, the instance weight implementation makes 'minInstancesPerNode' feature in trouble. The class weight implementation has no such issue, which will make the code more stable because very few inner modifications are needed. > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15344950#comment-15344950 ] Seth Hendrickson commented on SPARK-9478: - There has been a bit of confusion regarding this JIRA, I think. [~pcrenshaw] Please do correct me if I'm wrong, but the JIRA is, in truth, for adding a mechanism to handle imbalanced classification datasets. This could be done through class weighting or through instance weighting, I suppose the implementation is up for debate. There has been potentially more confusion since an initial PR was made using instance weighting. Now there is a PR made which adds class weighting. I think adding instance weighting is the best approach here because it allows users to handle imbalanced outcome classes in their data, but also adds the ability to use instance weighting generically which has a broad range of use cases. Additionally, it is not specific to classification. Also, this is how the other ML algorithms have so far dealt with it and it will allow forests and trees to conform to the same API as Logistic/Linear regression, for example. I vote to change this JIRA title to "Add instance weights for Random Forest and Decision Trees" and proceed accordingly, but I'm open to other opinions. If we want to pursue class weights we can do it in a separate JIRA. And again, I have a PR ready for this which I have not submitted because of a.) other blocking issues and b.) Spark 2.0 QA takes review precedence for the time being. I look forward to others' thoughts. Ping [~josephkb] (I cannot ping n-triple-a because I don't know the JIRA username). > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15344892#comment-15344892 ] Apache Spark commented on SPARK-9478: - User 'n-triple-a' has created a pull request for this issue: https://github.com/apache/spark/pull/13851 > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15254539#comment-15254539 ] Seth Hendrickson commented on SPARK-9478: - [~josephkb] I have a PR ready for this. It's being blocked by [SPARK-14610|https://issues.apache.org/jira/browse/SPARK-14610] and [SPARK-14599|https://issues.apache.org/jira/browse/SPARK-14599]. Is there any hope to get it in 2.0? > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15130323#comment-15130323 ] Fabian Boehnlein commented on SPARK-9478: - Thanks, [~meihuawu] woudl be very useful! Any ideas who could help review this? > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14946201#comment-14946201 ] Apache Spark commented on SPARK-9478: - User 'rotationsymmetry' has created a pull request for this issue: https://github.com/apache/spark/pull/9008 > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14945282#comment-14945282 ] Patrick Crenshaw commented on SPARK-9478: - [~meihuawu] No, I am not working on this. > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14942754#comment-14942754 ] Meihua Wu commented on SPARK-9478: -- [~pcrenshaw] Are you working on this? If not, I can send a PR based on [~josephkb]'s suggestions. > Add class weights to Random Forest > -- > > Key: SPARK-9478 > URL: https://issues.apache.org/jira/browse/SPARK-9478 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.4.1 >Reporter: Patrick Crenshaw > > Currently, this implementation of random forest does not support class > weights. Class weights are important when there is imbalanced training data > or the evaluation metric of a classifier is imbalanced (e.g. true positive > rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14653799#comment-14653799 ] Patrick Crenshaw commented on SPARK-9478: - If I work on this, should I wait until https://issues.apache.org/jira/browse/SPARK-3717 is finished? Add class weights to Random Forest -- Key: SPARK-9478 URL: https://issues.apache.org/jira/browse/SPARK-9478 Project: Spark Issue Type: Improvement Components: ML, MLlib Affects Versions: 1.4.1 Reporter: Patrick Crenshaw Currently, this implementation of random forest does not support class weights. Class weights are important when there is imbalanced training data or the evaluation metric of a classifier is imbalanced (e.g. true positive rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14647772#comment-14647772 ] Patrick Crenshaw commented on SPARK-9478: - Similar to this ticket for Logistic Regression https://issues.apache.org/jira/browse/SPARK-7685 and this one for SVMWithSGD https://issues.apache.org/jira/browse/SPARK-3246 Add class weights to Random Forest -- Key: SPARK-9478 URL: https://issues.apache.org/jira/browse/SPARK-9478 Project: Spark Issue Type: Improvement Components: ML, MLlib Affects Versions: 1.4.1 Reporter: Patrick Crenshaw Currently, this implementation of random forest does not support class weights. Class weights are important when there is imbalanced training data or the evaluation metric of a classifier is imbalanced (e.g. true positive rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9478) Add class weights to Random Forest
[ https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14648184#comment-14648184 ] Joseph K. Bradley commented on SPARK-9478: -- This sounds valuable. Handling it by reweighting examples (as is being done for logreg) seems like the simplest solution for now. I'll keep an eye on the ticket! Add class weights to Random Forest -- Key: SPARK-9478 URL: https://issues.apache.org/jira/browse/SPARK-9478 Project: Spark Issue Type: Improvement Components: ML, MLlib Affects Versions: 1.4.1 Reporter: Patrick Crenshaw Currently, this implementation of random forest does not support class weights. Class weights are important when there is imbalanced training data or the evaluation metric of a classifier is imbalanced (e.g. true positive rate at some false positive threshold). -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
