This issue occurs, if I turn the response variable to a categorical variable. If I get the variable as a numerical variable, the values are read correctly.
So I presume there is a fault in categorical conversion of the variable. On Tue, Aug 11, 2015 at 7:11 PM, Thushan Ganegedara <[email protected]> wrote: > I still get the same result > > 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 > 1.0 1.0 1.0 12.0 12.0 12.0 12.0 12.0 12.0 > 12.0 12.0 12.0 12.0 13.0 13.0 13.0 13.0 13.0 13.0 > 13.0 13.0 13.0 13.0 14.0 14.0 14.0 14.0 14.0 > 14.0 14.0 14.0 15.0 15.0 15.0 15.0 15.0 15.0 > 15.0 15.0 15.0 15.0 15.0 15.0 16.0 16.0 16.0 16.0 > 16.0 16.0 16.0 16.0 17.0 17.0 17.0 17.0 17.0 > 17.0 17.0 17.0 17.0 17.0 18.0 18.0 18.0 18.0 > 18.0 18.0 18.0 18.0 18.0 18.0 18.0 19.0 19.0 19.0 > 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 > 19.0 19.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 > 2.0 2.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 4.0 4.0 4.0 4.0 4.0 4.0 > 4.0 4.0 4.0 4.0 4.0 4.0 5.0 5.0 5.0 5.0 > 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 > 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 > 6.0 6.0 6.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 > 7.0 7.0 7.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 > 3.0 3.0 3.0 3.0 > > On Tue, Aug 11, 2015 at 7:05 PM, Nirmal Fernando <[email protected]> wrote: > >> Can you use following code and try; >> >> List<LabeledPoint> points = labeledPoints.collect(); >> for(int i=0;i<points.size();i++){ >> System.out.print(points.get(i).label() + "\t"); >> } >> >> On Tue, Aug 11, 2015 at 2:30 PM, Thushan Ganegedara <[email protected]> >> wrote: >> >>> I used the following snippet >>> >>> for(int i=0;i<labeledPoints.collect().size();i++){ >>> System.out.print(labeledPoints.collect().get(i).label() + >>> "\t"); >>> } >>> >>> in the public MLModel build() throws MLModelBuilderException in >>> DeeplearningModelBuilder.java >>> >>> >>> On Tue, Aug 11, 2015 at 6:17 PM, Nirmal Fernando <[email protected]> >>> wrote: >>> >>>> Hi thushan, >>>> >>>> We need more info. What did you exactly print and where? >>>> >>>> On Tue, Aug 11, 2015 at 12:47 PM, Thushan Ganegedara <[email protected]> >>>> wrote: >>>> >>>>> Hi, >>>>> >>>>> I found the potential cause of the poor accuracy for the leaf dataset. >>>>> It seems the data read into ML is wrong. >>>>> >>>>> I have attached the data file as a CSV (classes are in the last column) >>>>> >>>>> However, when I print out the labels of the read data (classes), it >>>>> looks something like below. Clearly there aren't this many "3.0" classes >>>>> and there should be classes up to 36.0. >>>>> >>>>> Is this caused by a bug? >>>>> >>>>> 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 >>>>> 1.0 1.0 1.0 1.0 12.0 12.0 12.0 12.0 12.0 >>>>> 12.0 12.0 12.0 12.0 12.0 13.0 13.0 13.0 13.0 >>>>> 13.0 13.0 >>>>> 13.0 13.0 13.0 13.0 14.0 14.0 14.0 14.0 >>>>> 14.0 14.0 14.0 14.0 15.0 15.0 15.0 15.0 15.0 >>>>> 15.0 15.0 15.0 15.0 15.0 15.0 15.0 16.0 16.0 >>>>> 16.0 16.0 >>>>> 16.0 16.0 16.0 16.0 17.0 17.0 17.0 17.0 >>>>> 17.0 17.0 17.0 17.0 17.0 17.0 18.0 18.0 18.0 >>>>> 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 19.0 >>>>> 19.0 19.0 >>>>> 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 >>>>> 19.0 19.0 19.0 2.0 2.0 2.0 2.0 2.0 2.0 >>>>> 2.0 2.0 2.0 2.0 2.0 2.0 2.0 3.0 3.0 >>>>> 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 4.0 4.0 4.0 4.0 4.0 >>>>> 4.0 4.0 4.0 4.0 4.0 4.0 4.0 5.0 5.0 >>>>> 5.0 5.0 >>>>> 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 >>>>> 5.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 >>>>> 6.0 6.0 6.0 6.0 7.0 7.0 7.0 7.0 7.0 >>>>> 7.0 7.0 >>>>> 7.0 7.0 7.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 >>>>> 3.0 3.0 >>>>> 3.0 3.0 3.0 3.0 >>>>> >>>>> -- >>>>> Regards, >>>>> >>>>> Thushan Ganegedara >>>>> School of IT >>>>> University of Sydney, Australia >>>>> >>>> >>>> >>>> >>>> -- >>>> >>>> Thanks & regards, >>>> Nirmal >>>> >>>> Team Lead - WSO2 Machine Learner >>>> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >>>> Mobile: +94715779733 >>>> Blog: http://nirmalfdo.blogspot.com/ >>>> >>>> >>>> >>> >>> >>> -- >>> Regards, >>> >>> Thushan Ganegedara >>> School of IT >>> University of Sydney, Australia >>> >> >> >> >> -- >> >> Thanks & regards, >> Nirmal >> >> Team Lead - WSO2 Machine Learner >> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >> Mobile: +94715779733 >> Blog: http://nirmalfdo.blogspot.com/ >> >> >> > > > -- > Regards, > > Thushan Ganegedara > School of IT > University of Sydney, Australia > -- Regards, Thushan Ganegedara School of IT University of Sydney, Australia
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