As a slight correction to my earlier post on running cvb from the trunk, the Nan values were my mistake. However, I still haven't had any success getting it to write document/topic inferences.
On Sat, Jun 22, 2013 at 7:21 AM, Mark Wicks <[email protected]> wrote: > 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
