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?
