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From: jkbradley <git@git.apache.org>
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Subject: [GitHub] spark issue #11119: [SPARK-10780][ML] Add an initial model to kmeans
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Message-Id: <20161024203653.631CBDFE80@git1-us-west.apache.org>
Date: Mon, 24 Oct 2016 20:36:53 +0000 (UTC)
archived-at: Mon, 24 Oct 2016 20:36:55 -0000

Github user jkbradley commented on the issue:

    https://github.com/apache/spark/pull/11119
  
    > Would you mind pointing me to an example of an algorithm which only copies some, but not all, of the estimator params?
    
    ALS is a good example: [https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala#L98]
    
    > [users identifying initialization method]
    
    I agree it's misleading to have mismatched Params initialModel and initMode, especially if Model.initialModel does not exist.  I'd say this is an ideal solution:
    * (in this PR) Have setInitialModel also set k, initMode, etc. (where we create a new initMode called "initialModel").
      * Calling setInitMode("initialModel") would probably need to throw an error.  This is a minor issue IMO.
    * (in a follow-up PR)  The above bullet point has one bigger issue: Setting initialModel via ```km.set(km.initialModel, initialModel)``` would bypass the setter method and therefore not set k, initMode, etc. appropriately.  This issue with tied Params has appeared elsewhere in MLlib as well.  We could implement a fix by having the ```Params.set``` method use Scala reflection to call the corresponding setter method.  We'd just have to take extra care to test this well.
      * There are some Params in Models without matching setter methods.  Those were added with the intention of having Estimator Params easily accessible from Models.  We'll just have to keep these in mind when writing unit tests.


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