Pca and ssvd propagates exact row keys given in the input. If you give it text keys, U and Usigma will have text keys. It doesn t change that. On Mar 10, 2014 3:39 AM, "Kevin Moulart" <[email protected]> wrote:
> Hi and thanks, I'll try that, but I'd like to do so using a mapreduce job > to improve performances. > > I'm using PCA as a way to reduce the dimension of the dataset both to > improve its relevance (with 1600+ variables, many of them are correlated) > and to improve the performances of the classification algorithm used. > > > > Kévin Moulart > > > 2014-03-10 9:45 GMT+01:00 Suneel Marthi <[email protected]>: > > > > > > > > > On Monday, March 10, 2014 4:21 AM, Kevin Moulart < > [email protected]> > > wrote: > > > > Its not clear to me from ur description as to the exact sequence of steps > > u r running thru, but an SSVD job requires a matrix as input (not a > > sequencefile of <Text, VectorWritables>. > > When u try running a seqdumper on ur SSVD output do u see anything? > > > > > > I see a Seqence File Text/VectorWritable with my original keys, and 99 > > valuesfor each element in my original dataset. > > > > The next step after u create ur sequencefiles of Vectors would be to run > > the rowId job to generate a matrix and docIndex. > > > > This matrix needs to be the input to SSVD (for dimensional reduction), > > > > > > Ok so I tried that and indeed the SSVD accepts the matrix as input and > > gives me a Sequence File IntWritable/VectorWritable. > > > > > > followed by train Naive Bayes and test Naive Bayes. > > > > > > Here it doesn't work anymore, the NB wants a Sequence File > > Text/VectorWritable, and it won't take the one created hereabove. > > Is there a counterpart to rowId that takes a matrix and docIndex outputs > > the SequenceFile ? > > > > >> Hmm... not that I know of. You are gonna have to write a utility > that > > reads docIndex and <IntWritable/VectorWritable> as inputs. > > a) Create a dictionary of documentId, documentName from docIndex > > b) > > (i) Read the Pair<Intwritable, VectorWritable> from the > > sequencefile<IntWritable,VectorWritable>, > > (ii) for each pair, read the key <IntWritable> and value > > <VectorWritable> { > > replace each key with the corresponding DocumentName > > <Text> from dictionary in (a) > > SequenceFile,Writer.write(Text, VectorWritable) > > } > > > > Question: I might have missed it but what's the reason again u r > > calling PCA for before running TrainNaiveBayes ? > > > > If others, have a better ideas please feel free to comment. > > > > > > Kévin Moulart > > > > > > 2014-03-07 16:23 GMT+01:00 Suneel Marthi <[email protected]>: > > > > Its not clear to me from ur description as to the exact sequence of steps > > u r running thru, but an SSVD job requires a matrix as input (not a > > sequencefile of <Text, VectorWritables>. > > > > When u try running a seqdumper on ur SSVD output do u see anything? > > > > The next step after u create ur sequencefiles of Vectors would be to run > > the rowId job to generate a matrix and docIndex. > > > > This matrix needs to be the input to SSVD (for dimensional reduction), > > followed by train Naive Bayes and test Naive Bayes. > > > > > > > > > > > > On Friday, March 7, 2014 10:01 AM, Kevin Moulart <[email protected] > > > > wrote: > > > > Hi again, > > > > I'm now using Mahout 0.9, and I'm trying to use PCA (via the SSVD) to > > reduce the dimention of a dataset from 1600+ features to ~100 and then to > > use the reducted dataset to train a naive bayes model and test it. > > > > So here is my workflow : > > > > - Transform my CSV into a SequencFile with > > > > key = class as Text (with a "/" in it to be accepted by NaiveBayes, so in > > the for "class/class") using a custom job in MapReduce. > > > > value = features as VectorWritable > > > > - Use mahout command line to reduce the dimension of the dataset : > > > > mahout ssvd -i /user/myCompny/Echant/echant100k.seq -o > > /user/myCompany/Echant/echant100k_red.seq --rank 100 -us -V false -U true > > -pca -ow -t 3 > > > > ==> Here I get - if I understand things correctly - U, being the reducted > > dataset. > > > > - Use mahout command line to train the NaiveBayes model : > > > > mahout trainnb -i /user/myCompany/Echant/echant100k_red.seq/U -o > > /user/myCompany/Echant/echant100k_red.model -l 0,1 > > -li /user/myCompany/Echant/labelIndex100k_red -ow > > > > > > - Use mahout command line to test the generated model : > > > > mahout testnb > > -i /user/myCompany/Echant/echant100k_red.seq/U --model > > /user/myCompany/Echant/echant100k_red.model -ow > > -o /user/myCompany/Echant/predicted_echant100k --labelIndex > > /user/myCompany/Echant/labelIndex100k_red > > > > (Here I test with the same dataset, but I should try with other datasets > as > > well once it runs smoothly) > > > > Here is my problem, everything seems to work quite well until I test my > > model : the output is full of NaN : > > > > > > Key: 1: Value: {0:NaN,1:NaN} > > Key: 1: Value: {0:NaN,1:NaN} > > Key: 0: Value: {0:NaN,1:NaN} > > Key: 0: Value: {0:NaN,1:NaN} > > Key: 1: Value: {0:NaN,1:NaN} > > Key: 0: Value: {0:NaN,1:NaN} > > Key: 1: Value: {0:NaN,1:NaN} > > Key: 0: Value: {0:NaN,1:NaN} > > Key: 0: Value: {0:NaN,1:NaN} > > Key: 0: Value: {0:NaN,1:NaN} > > Key: 1: Value: {0:NaN,1:NaN} > > > > > > I already have the same problem when training and testing with the full > > dataset but there, about 15% of the data still has values in output and > > gets predicted, the rest being NaN and unpredicted. > > > > Could you help me see what could be causing that ? > > > > Thanks in advance > > Bests, > > > > Kévin Moulart > > > > > > > > > > >
