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From Xiangrui Meng <men...@gmail.com>
Subject Re: Spark ML Pipeline inaccessible types
Date Fri, 27 Mar 2015 18:12:38 GMT
Hi Martin,

Could you attach the code snippet and the stack trace? The default
implementation of some methods uses reflection, which may be the
cause.

Best,
Xiangrui

On Wed, Mar 25, 2015 at 3:18 PM,  <zapletal-martin@email.cz> wrote:
> Thanks Peter,
>
> I ended up doing something similar. I however consider both the approaches
> you mentioned bad practices which is why I was looking for a solution
> directly supported by the current code.
>
> I can work with that now, but it does not seem to be the proper solution.
>
> Regards,
> Martin
>
> ---------- Původní zpráva ----------
> Od: Peter Rudenko <petro.rudenko@gmail.com>
> Komu: zapletal-martin@email.cz, Sean Owen <sowen@cloudera.com>
> Datum: 25. 3. 2015 13:28:38
>
>
> Předmět: Re: Spark ML Pipeline inaccessible types
>
>
> Hi Martin, here’s 2 possibilities to overcome this:
>
> 1) Put your logic into org.apache.spark package in your project - then
> everything would be accessible.
> 2) Dirty trick:
>
>  object SparkVector extends HashingTF {
>   val VectorUDT: DataType = outputDataType
> }
>
> then you can do like this:
>
>  StructType("vectorTypeColumn", SparkVector.VectorUDT, false))
>
> Thanks,
> Peter Rudenko
>
> On 2015-03-25 13:14, zapletal-martin@email.cz wrote:
>
> Sean,
>
> thanks for your response. I am familiar with NoSuchMethodException in
> general, but I think it is not the case this time. The code actually
> attempts to get parameter by name using val m =
> this.getClass.getMethodName(paramName).
>
> This may be a bug, but it is only a side effect caused by the real problem I
> am facing. My issue is that VectorUDT is not accessible by user code and
> therefore it is not possible to use custom ML pipeline with the existing
> Predictors (see the last two paragraphs in my first email).
>
> Best Regards,
> Martin
>
> ---------- Původní zpráva ----------
> Od: Sean Owen <sowen@cloudera.com>
> Komu: zapletal-martin@email.cz
> Datum: 25. 3. 2015 11:05:54
> Předmět: Re: Spark ML Pipeline inaccessible types
>
>
> NoSuchMethodError in general means that your runtime and compile-time
> environments are different. I think you need to first make sure you
> don't have mismatching versions of Spark.
>
> On Wed, Mar 25, 2015 at 11:00 AM, <zapletal-martin@email.cz> wrote:
>> Hi,
>>
>> I have started implementing a machine learning pipeline using Spark 1.3.0
>> and the new pipelining API and DataFrames. I got to a point where I have
>> my
>> training data set prepared using a sequence of Transformers, but I am
>> struggling to actually train a model and use it for predictions.
>>
>> I am getting a java.lang.NoSuchMethodException:
>> org.apache.spark.ml.regression.LinearRegression.myFeaturesColumnName()
>> exception thrown at checkInputColumn method in Params trait when using a
>> Predictor (LinearRegression in my case, but that should not matter). This
>> looks like a bug - the exception is thrown when executing
>> getParam(colName)
>> when the require(actualDataType.equals(datatype), ...) requirement is not
>> met so the expected requirement failed exception is not thrown and is
>> hidden
>> by the unexpected NoSuchMethodException instead. I can raise a bug if this
>> really is an issue and I am not using something incorrectly.
>>
>> The problem I am facing however is that the Predictor expects features to
>> have VectorUDT type as defined in Predictor class (protected def
>> featuresDataType: DataType = new VectorUDT). But since this type is
>> private[spark] my Transformer can not prepare features with this type
>> which
>> then correctly results in the exception above when I use a different type.
>>
>> Is there a way to define a custom Pipeline that would be able to use the
>> existing Predictors without having to bypass the access modifiers or
>> reimplement something or is the pipelining API not yet expected to be used
>> in this way?
>>
>> Thanks,
>> Martin
>>
>>
>
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