Hi Abdelhakim,

Thanks for the link! I ended up saving the "Dataset" into file after
loading the training data, works perfectly :)

Yuhan

On Tue, Aug 14, 2012 at 12:44 PM, Abdelhakim Deneche <[email protected]>wrote:

> Hi Yuhan,
>
> If you follow the example in the wiki page:
>
> https://cwiki.apache.org/MAHOUT/partial-implementation.html
>
> You should have generated a descriptor file for your training data using
> the "Describe" tool, because the test data must have similar attributes,
> you can just load that descriptor using
>
> Dataset.load()
>
>
>
>
> On 14 août 2012, at 19:39, Yuhan Zhang <[email protected]> wrote:
>
> > Hi Deneche and Chyi-Kwei,
> >
> > Thanks for the suggestion. I'm reading the "TestForest" class (
> >
> https://github.com/apache/mahout/blob/trunk/core/src/main/java/org/apache/mahout/classifier/df/mapreduce/Classifier.java
> > )
> >
> >
> > Looks like I'm using a different method to load the dataset
> >    Dataset dataset = DataLoader.generateDataset( pattern, false,
> > trainDataArray);
> >
> > If I use a similar way to load the dataset for testing, it gives me
> > "unknown" as a result: (but works correctly if I use the trainData as
> > dataset)
> >   Dataset dataset = DataLoader.generateDataset(pattern, false, testData
> );
> >
> > while the example code is loading using:
> >    Dataset.load(conf, datasetPath)
> >
> > Looks like it has to do the way I'm generating the test dataset. Thanks
> for
> > the help. will look more into it.
> >
> > Yuhan
> >
> > On Mon, Aug 13, 2012 at 9:28 PM, deneche abdelhakim <[email protected]
> >wrote:
> >
> >> Last time I checked it was still working in the latest version
> >>
> >> On Tue, Aug 14, 2012 at 4:26 AM, chyi-kwei yau <[email protected]
> >>> wrote:
> >>
> >>> Hi Yuhan,
> >>>
> >>> You can run "BuildForest" on your train data and "TestForest" on your
> >>> testing data.
> >>>
> >>> You can check the example here:
> >>> https://cwiki.apache.org/MAHOUT/partial-implementation.html
> >>>
> >>> I use mahout 0.6 and it works for me, but not sure it will work in
> >>> other mahout version.
> >>>
> >>> Best,
> >>> Chyi-Kwei Yau
> >>>
> >>> On Mon, Aug 13, 2012 at 8:11 PM, Yuhan Zhang <[email protected]>
> >> wrote:
> >>>> some typo in the last email:
> >>>> name of the method is decisionForest.classify(dataset, random,
> >> Instance)
> >>>>
> >>>> if a dataset other than training dataset is given, it will result
> >>>> IllegalArgumentException:values not found for attribute 1.
> >>>>
> >>>> need some help here.
> >>>>
> >>>> Yuhan
> >>>>
> >>>> On Mon, Aug 13, 2012 at 5:03 PM, Yuhan Zhang <[email protected]>
> >>> wrote:
> >>>>
> >>>>> Hi all,
> >>>>>
> >>>>> I'm  trying to train a decision forest, save it to file, and use it
> >>> latter.
> >>>>> I have managed to write a trained decision forest to file using
> >>>>> "DecisionForest.write( dataOutPut ) ";
> >>>>>
> >>>>> but when I load a saved decision tree from file  to classify, I
> >> realized
> >>>>> the the method
> >>>>> DecisionForest.classifier(Dataset, random, Instance) is expecting the
> >>>>> original training Dataset.
> >>>>>
> >>>>> Is there a way to avoid loading the training Dataset? It is kind
> >> large,
> >>>>> and I'd like to avoid loading it.
> >>>>>
> >>>>>
> >>>>> Thank you
> >>>>>
> >>>>> Yuhan
> >>>>
> >>>>
> >>>>
> >>>>
> >>>> --
> >>>> Yuhan Zhang
> >>>> Senior Software Engineer
> >>>> OneScreen Inc.
> >>>> [email protected] <[email protected]>
> >>>> www.onescreen.com
> >>>> (949) 525-4825 Ext: 177
> >>>>
> >>>>
> >>>> The information contained in this e-mail is for the exclusive use of
> >> the
> >>>> intended recipient(s) and may be confidential, proprietary, and/or
> >>> legally
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> >>>> waiver of any privilege.  If you receive this message in error, please
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> >>>
> >>
> >
> >
> >
> > --
> > Yuhan Zhang
> > Senior Software Engineer
> > OneScreen Inc.
> > [email protected] <[email protected]>
> > www.onescreen.com
> > (949) 525-4825 Ext: 177
> >
> >
> > The information contained in this e-mail is for the exclusive use of the
> > intended recipient(s) and may be confidential, proprietary, and/or
> legally
> > privileged. Inadvertent disclosure of this message does not constitute a
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