[jira] [Commented] (SPARK-13677) Support Tree-Based Feature Transformation for ML

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-13677?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15796748#comment-15796748 ] 

Joseph K. Bradley commented on SPARK-13677:
-------------------------------------------

[~podongfeng] Apologies for the inaction on this, but I agree with you about redoing this for the DataFrame-based API.  Could you please propose an API here before implementing it?  Thanks!

> Support Tree-Based Feature Transformation for ML
> ------------------------------------------------
>
>                 Key: SPARK-13677
>                 URL: https://issues.apache.org/jira/browse/SPARK-13677
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: zhengruifeng
>            Priority: Minor
>
> It would be nice to be able to use RF and GBT for feature transformation:
> First fit an ensemble of trees (like RF, GBT or other TreeEnsambleModels) on the training set. Then each leaf of each tree in the ensemble is assigned a fixed arbitrary feature index in a new feature space. These leaf indices are then encoded in a one-hot fashion.
> This method was first introduced by facebook(http://www.herbrich.me/papers/adclicksfacebook.pdf), and is implemented in two famous library:
> sklearn (http://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#example-ensemble-plot-feature-transformation-py)
> xgboost (https://github.com/dmlc/xgboost/blob/master/demo/guide-python/predict_leaf_indices.py)
> I have implement it in mllib:
> val features : RDD[Vector] = ...
> val model1 : RandomForestModel = ...
> val transformed1 : RDD[Vector] = model1.leaf(features)
> val model2 : GradientBoostedTreesModel = ...
> val transformed2 : RDD[Vector] = model2.leaf(features)



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