Hi Kirsti,
I think you attached some images to your file which show the code.
Unfortunately this is not supported by the mailing list. So maybe you could
resend what you’ve already tried.
In order to access the ALS model, you can do the following:
val als = ALS()
als.fit(input)
val (userFactorsOpt, itemFactorsOpt) = als.factorsOption
val factorsTypeInfo = TypeInformation.of(classOf[Factors])
val factorsSerializer = factorsTypeInfo.createSerializer(new ExecutionConfig())
val outputFormat = new TypeSerializerOutputFormat[Factors]
userFactorsOpt match {
case Some(userFactors) => userFactors.write(outputFormat, "user_path")
case None =>
}
itemFactorsOpt match {
case Some(itemFactors) => itemFactors.write(outputFormat, "item_path")
case None =>
}
Cheers,
Till
On Tue, Apr 12, 2016 at 10:29 AM, KirstiLaurila <kirsti.laur...@rovio.com>
wrote:
> How should this be done for the recommendation engine (that is ALS, example
> here
>
> https://ci.apache.org/projects/flink/flink-docs-release-1.0/apis/batch/libs/ml/als.html
> <
> https://ci.apache.org/projects/flink/flink-docs-release-1.0/apis/batch/libs/ml/als.html
> >
> ).
>
> I am able to run the example with my example data but cannot get anything
> written to any file (user or item matrices).
>
> Basically, I have tried something like this
>
>
>
>
> Tried also to apply similar approach than this
>
>
>
> but with no success. Could someone help me with this to get my model saved?
>
>
> Best,
> Kirsti
>
>
>
> Trevor Grant wrote
> > I'm just about to open an issue / PR solution for 'warm-starts'
> >
> > Once this is in, we could just add a setter for the weight vector (and
> > what
> > ever iteration you're on if you're going to do more partial fits).
> >
> > Then all you need to save if your weight vector (and iter number).
> >
> >
> >
> > Trevor Grant
> > Data Scientist
> > https://github.com/rawkintrevo
> > http://stackexchange.com/users/3002022/rawkintrevo
> > http://trevorgrant.org
> >
> > *"Fortunate is he, who is able to know the causes of things." -Virgil*
> >
> >
> > On Fri, Apr 8, 2016 at 9:04 AM, Behrouz Derakhshan <
>
> > behrouz.derakhshan@
>
> >> wrote:
> >
> >> 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 <
>
> > trohrmann@
>
> > >
> >> 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 <
>
> > smarthi@
>
> > >
> >>> 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.phetsarath@
>
> >> 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 <
>
> > trohrmann@
>
> > >
> >>>>> 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.robutti@
>
> >> 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.phetsarath@
>
> >>:
> >>>>>>>
> >>>>>>>> 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>*
> >>>>>
> >>>>
> >>>>
> >>>
> >>
>
>
>
>
>
> --
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