I found a helper class that I think should do the trick. Take a look at
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
When passing the Loss, you should be able to do something like:
Losses.fromString("leastSquaresError")
On Mon, Feb 29, 2016 at 10:03 AM, diplomatic Guru <diplomaticg...@gmail.com>
wrote:
> It's strange as you are correct the doc does state it. But it's
> complaining about the constructor.
>
> When I clicked on the org.apache.spark.mllib.tree.loss.AbsoluteError
> class, this is what I see:
>
>
> @Since("1.2.0")
> @DeveloperApi
> object AbsoluteError extends Loss {
>
> /**
> * Method to calculate the gradients for the gradient boosting
> calculation for least
> * absolute error calculation.
> * The gradient with respect to F(x) is: sign(F(x) - y)
> * @param prediction Predicted label.
> * @param label True label.
> * @return Loss gradient
> */
> @Since("1.2.0")
> override def gradient(prediction: Double, label: Double): Double = {
> if (label - prediction < 0) 1.0 else -1.0
> }
>
> override private[mllib] def computeError(prediction: Double, label:
> Double): Double = {
> val err = label - prediction
> math.abs(err)
> }
> }
>
>
> On 29 February 2016 at 15:49, Kevin Mellott <kevin.r.mell...@gmail.com>
> wrote:
>
>> Looks like it should be present in 1.3 at
>> org.apache.spark.mllib.tree.loss.AbsoluteError
>>
>>
>> spark.apache.org/docs/1.3.0/api/java/org/apache/spark/mllib/tree/loss/AbsoluteError.html
>>
>> On Mon, Feb 29, 2016 at 9:46 AM, diplomatic Guru <
>> diplomaticg...@gmail.com> wrote:
>>
>>> AbsoluteError() constructor is undefined.
>>>
>>> I'm using Spark 1.3.0, maybe it is not ready for this version?
>>>
>>>
>>>
>>> On 29 February 2016 at 15:38, Kevin Mellott <kevin.r.mell...@gmail.com>
>>> wrote:
>>>
>>>> I believe that you can instantiate an instance of the AbsoluteError
>>>> class for the *Loss* object, since that object implements the Loss
>>>> interface. For example.
>>>>
>>>> val loss = new AbsoluteError()
>>>> boostingStrategy.setLoss(loss)
>>>>
>>>> On Mon, Feb 29, 2016 at 9:33 AM, diplomatic Guru <
>>>> diplomaticg...@gmail.com> wrote:
>>>>
>>>>> Hi Kevin,
>>>>>
>>>>> Yes, I've set the bootingStrategy like that using the example. But I'm
>>>>> not sure how to create and pass the Loss object.
>>>>>
>>>>> e.g
>>>>>
>>>>> boostingStrategy.setLoss(......);
>>>>>
>>>>> Not sure how to pass the selected Loss.
>>>>>
>>>>> How do I set the Absolute Error in setLoss() function?
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On 29 February 2016 at 15:26, Kevin Mellott <kevin.r.mell...@gmail.com
>>>>> > wrote:
>>>>>
>>>>>> You can use the constructor that accepts a BoostingStrategy object,
>>>>>> which will allow you to set the tree strategy (and other hyperparameters
>>>>>> as
>>>>>> well).
>>>>>>
>>>>>> *GradientBoostedTrees
>>>>>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/tree/GradientBoostedTrees.html#GradientBoostedTrees(org.apache.spark.mllib.tree.configuration.BoostingStrategy)>*
>>>>>> (BoostingStrategy
>>>>>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/tree/configuration/BoostingStrategy.html>
>>>>>> boostingStrategy)
>>>>>>
>>>>>> On Mon, Feb 29, 2016 at 9:21 AM, diplomatic Guru <
>>>>>> diplomaticg...@gmail.com> wrote:
>>>>>>
>>>>>>> Hello guys,
>>>>>>>
>>>>>>> I think the default Loss algorithm is Squared Error for regression,
>>>>>>> but how do I change that to Absolute Error in Java.
>>>>>>>
>>>>>>> Could you please show me an example?
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
