I tried with cvb from trunk and ran into several problems: 1) The topic/term distributions were all Nan. 2) The initial perplexity was Nan. 3) It never wrote the document/topic inferences. 4) It exited with an exception stating that the topic/term distribution output directory already exists, after successfully creating it and writing to it. It did not exist before running cvb.
On Thu, Jun 20, 2013 at 10:18 PM, Jake Mannix <[email protected]> wrote: > There was a bug in Mahout 0.7 regarding the doc/topic outputs, > can you try your little test on trunk, and see if you get a more > sensible / interpretable result? > > > On Thu, Jun 20, 2013 at 10:17 AM, Mark Wicks <[email protected]> wrote: > >> I apologize for posting this again. I sent it during the weekend and >> didn't get any response (which seems unusual for this list :)). >> I am hoping that someone with some LDA/cvb experience who can help >> might have missed it over the weekend. >> Can someone tell me (1) if the document-topic distribution below makes >> sense for the term frequencies shown and (2) how I should interpret >> it. >> >> Mark Wicks >> >> On Sat, Jun 15, 2013 at 9:22 AM, Mark Wicks <[email protected]> wrote: >> > I am having trouble interpreting the "doc-topic" distribution produced >> > by the cvb implementation of LDA in Mahout 0.7. Here's the >> > term-frequency matrix for a simple test case (shown here as the output >> > of mahout seqdumper): >> > >> > Key: /d01: Value: /d01:{0:30.0,1:10.0} >> > Key: /d02: Value: /d02:{0:60.0,1:20.0} >> > Key: /d03: Value: /d03:{0:30.0,1:10.0} >> > Key: /d04: Value: /d04:{0:60.0,1:20.0} >> > Key: /x01: Value: /x01:{2:30.0,3:10.0} >> > Key: /x02: Value: /x02:{2:60.0,3:20.0} >> > Key: /x03: Value: /x03:{2:30.0,3:10.0} >> > Count: 7 >> > >> > The intent here was that the d01 through d04 documents would consist >> almost >> > entirely of one topic represented almost entirely by terms 0 and 1 >> > with a topic-term >> > distribution of [0.75, 0.25, epsilon, epsilon] and that the x01 >> > through x03 documents >> > would consist almost entirely of a second topic represented almost >> entirely by >> > terms 2 and 3 with a topic-term distribution of [epsilon, epsilon, >> > 0.75, 0.25]. Since >> > the "d" documents do not contain terms 2 or 3 and the "x" documents do >> > not contain >> > terms 0 or 1, I expected to see document topic distributions that were >> > approximately >> > equal to >> > >> > d01: 1 0 >> > d01: 1 0 >> > d02: 1 0 >> > d03: 1 0 >> > x01: 0 1 >> > x02: 0 1 >> > x03: 0 1 >> > >> > I ran the following command (where the simplelda/sparse/matrix directory >> > contained the previous term frequency matrix). The algorithm ran to >> completion >> > (meaning that it converged before the maximum number of iterations was >> > exceeded). >> > >> > mahout cvb \ >> > -i simplelda/sparse/matrix \ >> > -dict simplelda/sparse/dictionary.file-0 \ >> > -ow -o simplelda/cvb-topics \ >> > -dt simplelda/cvb-classifications \ >> > -tf 0.25 \ >> > -block 4 \ >> > -x 20 \ >> > -cd 1e-10 \ >> > -k 2 \ >> > --tempDir simplelda/temp-k2 \ >> > -seed 6956 >> > >> > The topic-term frequencies written to simplelda/cvb-topics were accurate >> and as >> > expected: >> > >> > >> {0:0.7499999999895863,1:0.2499999999548601,2:2.7776873636508568E-11,3:2.777682733874987E-11} >> > >> {0:9.375466996550278E-11,1:9.375456577819702E-11,2:0.7499999998802006,3:0.24999999993229008} >> > >> > However, the document-topic distribution output written to >> > simplelda/cvbclassifications was not at all what I expected: >> > >> > Key: 0: Value: {0:0.05705773500297721,1:0.9429422649970228} >> > Key: 1: Value: {0:0.05705773500297721,1:0.9429422649970228} >> > Key: 2: Value: {0:0.05705773500297721,1:0.9429422649970228} >> > Key: 3: Value: {0:0.05705773500297721,1:0.9429422649970228} >> > Key: 4: Value: {0:0.4335650246424872,1:0.5664349753575127} >> > Key: 5: Value: {0:0.4335650246424872,1:0.5664349753575127} >> > Key: 6: Value: {0:0.4335650246424872,1:0.5664349753575127} >> > Count: 7 >> > >> > These are called "doc-topic distributions" in the help output, so I >> > interpreted this to >> > mean that the estimator concluded the "d" document terms were most >> likely all >> > drawn from the second topic. But the "d" documents contain no terms from >> the >> > second topic! Likewise, the "x" documents contain no terms from the >> > first topic, so >> > why is there a relatively large value (0.4335) in the first column. If >> > this document- >> > topic distribution produced by cvb is correct, what does it represent? >> > > > > -- > > -jake
