Is there a reasons the Predictor or Estimator class don't have read and
write methods for saving and retrieving the model? I couldn't find Jira
issues for it. Does it make sense to create one ?
BR,
Behrouz
On Wed, Mar 30, 2016 at 4:40 PM, Till Rohrmann <trohrm...@apache.org> wrote:
> Yes Suneel is completely wright. If the data does not implement
> IOReadableWritable it is probably easier to use the
> TypeSerializerOutputFormat. What you need here to seralize the data is a
> TypeSerializer. You can obtain it the following way:
>
> val model = mlr.weightsOption.get
>
> val weightVectorTypeInfo = TypeInformation.of(classOf[WeightVector])
> val weightVectorSerializer = weightVectorTypeInfo.createSerializer(new
> ExecutionConfig())
> val outputFormat = new TypeSerializerOutputFormat[WeightVector]
> outputFormat.setSerializer(weightVectorSerializer)
>
> model.write(outputFormat, "path")
>
> Cheers,
> Till
>
>
> On Tue, Mar 29, 2016 at 8:22 PM, Suneel Marthi <smar...@apache.org> wrote:
>
>> U may want to use FlinkMLTools.persist() methods which use
>> TypeSerializerFormat and don't enforce IOReadableWritable.
>>
>>
>>
>> On Tue, Mar 29, 2016 at 2:12 PM, Sourigna Phetsarath <
>> gna.phetsar...@teamaol.com> wrote:
>>
>>> Till,
>>>
>>> Thank you for your reply.
>>>
>>> Having this issue though, WeightVector does not extend IOReadWriteable:
>>>
>>> *public* *class* SerializedOutputFormat<*T* *extends* IOReadableWritable
>>> >
>>>
>>> *case* *class* WeightVector(weights: Vector, intercept: Double)
>>> *extends* Serializable {}
>>>
>>>
>>> However, I will use the approach to write out the weights as text.
>>>
>>>
>>> On Tue, Mar 29, 2016 at 5:01 AM, Till Rohrmann <trohrm...@apache.org>
>>> wrote:
>>>
>>>> Hi Gna,
>>>>
>>>> there are no utilities yet to do that but you can do it manually. In
>>>> the end, a model is simply a Flink DataSet which you can serialize to
>>>> some file. Upon reading this DataSet you simply have to give it to
>>>> your algorithm to be used as the model. The following code snippet
>>>> illustrates this approach:
>>>>
>>>> mlr.fit(inputDS, parameters)
>>>>
>>>> // write model to disk using the SerializedOutputFormat
>>>> mlr.weightsOption.get.write(new SerializedOutputFormat[WeightVector],
>>>> "path")
>>>>
>>>> // read the serialized model from disk
>>>> val model = env.readFile(new SerializedInputFormat[WeightVector], "path")
>>>>
>>>> // set the read model for the MLR algorithm
>>>> mlr.weightsOption = model
>>>>
>>>> Cheers,
>>>> Till
>>>>
>>>>
>>>> On Tue, Mar 29, 2016 at 10:46 AM, Simone Robutti <
>>>> simone.robu...@radicalbit.io> wrote:
>>>>
>>>>> To my knowledge there is nothing like that. PMML is not supported in
>>>>> any form and there's no custom saving format yet. If you really need a
>>>>> quick and dirty solution, it's not that hard to serialize the model into a
>>>>> file.
>>>>>
>>>>> 2016-03-28 17:59 GMT+02:00 Sourigna Phetsarath <
>>>>> gna.phetsar...@teamaol.com>:
>>>>>
>>>>>> Flinksters,
>>>>>>
>>>>>> Is there an example of saving a Trained Model, loading a Trained
>>>>>> Model and then scoring one or more feature vectors using Flink ML?
>>>>>>
>>>>>> All of the examples I've seen have shown only sequential fit and
>>>>>> predict.
>>>>>>
>>>>>> Thank you.
>>>>>>
>>>>>> -Gna
>>>>>> --
>>>>>>
>>>>>>
>>>>>> *Gna Phetsarath*System Architect // AOL Platforms // Data Services
>>>>>> // Applied Research Chapter
>>>>>> 770 Broadway, 5th Floor, New York, NY 10003
>>>>>> o: 212.402.4871 // m: 917.373.7363
>>>>>> vvmr: 8890237 aim: sphetsarath20 t: @sourigna
>>>>>>
>>>>>> * <http://www.aolplatforms.com>*
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>>
>>> --
>>>
>>>
>>> *Gna Phetsarath*System Architect // AOL Platforms // Data Services //
>>> Applied Research Chapter
>>> 770 Broadway, 5th Floor, New York, NY 10003
>>> o: 212.402.4871 // m: 917.373.7363
>>> vvmr: 8890237 aim: sphetsarath20 t: @sourigna
>>>
>>> * <http://www.aolplatforms.com>*
>>>
>>
>>
>
