Hi Theodore,
Thanks for explaining the reason. :)
So how about change LabeledVector contains two vectors? One of vectors is for
label and the other one is for value. I think this approach would be okay
because a double value label could be represented as a
DenseVector(Array(LABEL_VALUE)).
Only problem in this approach is some overhead of processing Vector type in
case of single double label. If the overhead is significant, we should create
two types of LabeledVector such as DoubleLabeledVector and VectorLabeledVector.
Which one is preferred?
> On Jan 5, 2016, at 11:38 PM, Theodore Vasiloudis
> <theodoros.vasilou...@gmail.com> wrote:
>
> Generalizing the type of the label for the label vector is an idea we
> played with when designing the current optimization framework.
>
> We ended up deciding against it as the double type allows us to do
> regressions and (multiclass) classification which should be the majority of
> the use cases out there, while keeping the code simple.
>
> Generalizing this to [T <: Serializable] is too broad I think. [T <:
> Vector] is I think more reasonable, I cannot think of many cases where the
> label in an optimization problems is something other than a vector/double.
>
> Any change would require a number of changes in the optimization of course,
> as optimizing for vector and double labels requires different handling of
> error calculation etc but it should be doable.
> Note however that since LabeledVector is such a core part of the library
> any changes would involve a number of adjustments downstream.
>
> Perhaps having different optimizers etc. for Vectors and double labels
> makes sense, but I haven't put much though into this.
>
>
> On Tue, Jan 5, 2016 at 12:17 PM, Chiwan Park <chiwanp...@apache.org> wrote:
>
>> Hi Hilmi,
>>
>> Thanks for suggestion about type of labeled vector. Basically, I agree
>> that your suggestion is reasonable. But, I would like to generialize
>> `LabeledVector` like following example:
>>
>> ```
>> case class LabeledVector[T <: Serializable](label: T, vector: Vector)
>> extends Serializable {
>> // some implementations for LabeledVector
>> }
>> ```
>>
>> How about this implementation? If there are any other opinions, please
>> send a email to mailing list.
>>
>>> On Jan 5, 2016, at 7:36 PM, Hilmi Yildirim <hilmi.yildi...@dfki.de>
>> wrote:
>>>
>>> Hi,
>>> in the ML-Pipeline of Flink we have the "LabeledVector" class. It
>> consists of a vector and a label as a double value. Unfortunately, it is
>> not applicable for sequence learning where the label is also a vector. For
>> example, in NLP we have a vector of words and the label is a vector of the
>> corresponding labels.
>>>
>>> The optimize function of the "Solver" class has a DateSet[LabeledVector]
>> as input and, therefore, it is not applicable for sequence learning. I
>> think the LabeledVector should be adapted that the label is a vector
>> instead of a single Double value. What do you think?
>>>
>>> Best Regards,
>>>
>>> --
>>> ==================================================================
>>> Hilmi Yildirim, M.Sc.
>>> Researcher
>>>
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>>
>> Regards,
>> Chiwan Park
Regards,
Chiwan Park
