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
