mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Dmitriy Lyubimov <>
Subject Re: SSVD for dimensional reduction + Kmeans
Date Fri, 10 Aug 2012 21:48:31 GMT
another piece of information is that according to investigation we
did, using q=0 seems to underestimate singular values somewhat and
produces a little better decay than optimal values would have. which
means variance retained would err on the optimistic side a bit
(showing a little bit more variance retained than actual).

also i think retaining 99% is reasonable with big corpuses. this
desirable threshold could probably be reduced a little bit to
accomodate pragmatic resource capacity.

On Fri, Aug 10, 2012 at 2:42 PM, Dmitriy Lyubimov <> wrote:
> note that to estimate variance retained approximately only, you
> probably don't need to run it with q=1 so you can run with q=0 and not
> request either V or U but singular values only. That will reduce
> running time dramatically (perhaps up to 2-5 times faster compared to
> with q=1 and U and V).
> On Fri, Aug 10, 2012 at 2:31 PM, Dmitriy Lyubimov <> wrote:
>> With PCA, there's a metric, something called "variance retained".
>> One idea of mine to estimate k is described in footnote discussion on
>> page 5. While it is not possible to compute "PCA variance retained"
>> metric exactly with an application of a thin SVD (the metric assumes
>> use of a full rank SVD) it is possible to infer upper estimate for k
>> given target variance retained (say, 99%) or try some sort of
>> polynomialy approximated value for the sum of all singular values
>> given visible decay. Probably requires some simple code in R or matlab
>> to get reasonable estimate.
>> This technique requires running  PCA one time and then estimate
>> sufficient k given singular values produced on your corpus. If the
>> action is repetetive and corpus is not changing drastically, you may
>> infer if you spending too much (or too little) on k for future uses.
>> But pragmatically i just use the best k my cluster can compute in the
>> time i need. my corpus is relatively small and i don't run full corpus
>> run too often so i can afford some time spent.
>> On Fri, Aug 10, 2012 at 2:14 PM, Pat Ferrel <> wrote:
>>> The built-in PCA option is one reason I wanted to try SSVD. Building the test
was to make sure I understood the external matrix operations before diving in. I expect one
primary decision is how to choose k for reduction. I'm hoping to get some noise rejection
so not using it for reduced matrix size so much. We are starting with m = 500,000 and a million
or so docs. We get many dups and low value docs in a small web crawl, so lots of noise.
>>> You mention in  your paper:
>>> "The valueof k + p directly impacts running time and memory requirements.
>>> k+p=500 is probably more than reasonable. Typically k + p
>>> is taken within range 20…200"
>>> So I guess we might start with
>>>         -p 15 (default)
>>>         -q 1
>>>         -k 200
>>> Is there any use in hand inspecting the eigen vectors before choosing the final
k? If so do you get those by choosing k nearly = m or is something like k = 1000 (or ?) good
enough to for inspection?
>>> On Aug 10, 2012, at 12:53 PM, Dmitriy Lyubimov <> wrote:
>>> BTW if you really are trying to reduce dimensionality, you may want to
>>> consider --pca option with SSVD, that [i think] will provide with much
>>> better preserved data variance then just clean SVD (i.e. essentially
>>> run a PCA space transformation on your data rather than just SVD)
>>> -d
>>> On Fri, Aug 10, 2012 at 11:57 AM, Pat Ferrel <> wrote:
>>>> Got it. Well on to some real and much larger data sets then…
>>>> On Aug 10, 2012, at 11:53 AM, Dmitriy Lyubimov <>
>>>> i think actually Mahout's Lanczos requires external knowledge of input
>>>> size too, in part for similar reasons. SSVD doesn't because it doesn't
>>>> have "other" reasons to know input size but fundamental assumption
>>>> rank(input)>=rank(thin SVD) still stands about the input but the
>>>> method doesn't have a goal of verifying it explicitly (which would be
>>>> kind of hard), and instead either produces 0 eigenvectors or runs into
>>>> block deficiency.
>>>> It is however hard to assert whether block deficiency stemmed from
>>>> input size deficiency vs. split size deficiency, and neither of
>>>> situations is typical for a real-life SSVD applications, hence error
>>>> message is somewhat vague.
>>>> On Fri, Aug 10, 2012 at 11:39 AM, Dmitriy Lyubimov <>
>>>>> The easy answer is to ensure (k+p)<= m. It is mathematical constraint,
>>>>> not a method pecularity.
>>>>> The only reason the solution doesn't warn you explicitly is because
>>>>> DistributedRowMatrix format, which is just a sequence file of rows,
>>>>> would not provide us with an easy way to verify what m actually is
>>>>> before it actually iterates over it and runs into block size
>>>>> deficiency. So if you now m as an external knowledge, it is easy to
>>>>> avoid being trapped by block height defiicency.
>>>>> On Fri, Aug 10, 2012 at 11:32 AM, Pat Ferrel <>
>>>>>> This is only a test with some trivially simple data. I doubt there
are any splits and yes it could easily be done in memory but that is not the purpose. It is
based on testKmeansDSVD2, which is in
>>>>>> mahout/integration/src/test/java/org/apache/mahout/clustering/
>>>>>> I've attached the modified and running version with testKmeansDSSVD
>>>>>> As I said I don't think this is a real world test. It tests that
the code runs, and it does. Getting the best results is not part of the scope. I just thought
if there was an easy answer I could clean up the parameters for SSVDSolver.
>>>>>> Since it is working I don't know that it's worth the effort unless
people are likely to run into this with larger data sets.
>>>>>> Thanks anyway.
>>>>>> On Aug 10, 2012, at 11:07 AM, Dmitriy Lyubimov <>
>>>>>> It happens because of internal constraints stemming from blocking.
>>>>>> happens when a split of A (input) has less than (k+p) rows at which
>>>>>> point blocks are too small (or rather, to short) to successfully
>>>>>> perform a QR on .
>>>>>> This also means, among other things, k+p cannot be more than your
>>>>>> total number of rows in the input.
>>>>>> It is also possible that input A is way too wide or k+p is way too
>>>>>> so that an arbitrary split does not fetch at least k+p rows of A,
>>>>>> in practice i haven't seen such cases in practice yet. If that
>>>>>> happens, there's an option to increase minSplitSize (which would
>>>>>> undermine MR mappers efficiency  somewhat). But i am pretty sure
it is
>>>>>> not your case.
>>>>>> But if your input is shorter than k+p, then it is a case too small
>>>>>> SSVD. in fact, it probably means you can solve test directly in memory
>>>>>> with any solver. You can still use SSVD with k=m and p=0 (I think)
>>>>>> this case and get exact (non-reduced rank) decomposition equivalent
>>>>>> with no stochastic effects, but that is not what it is for really.
>>>>>> Assuming your input is m x n, can you tell me please what your m,
n, k
>>>>>> and p are?
>>>>>> thanks.
>>>>>> -D
>>>>>> On Fri, Aug 10, 2012 at 9:21 AM, Pat Ferrel <>
>>>>>>> There seems to be some internal constraint on k and/or p, which
is making a test difficult. The test has a very small input doc set and choosing the wrong
k it is very easy to get a failure with this message:
>>>>>>> java.lang.IllegalArgumentException: new m can't be less than
>>>>>>>       at org.apache.mahout.math.hadoop.stochasticsvd.qr.GivensThinSolver.adjust(
>>>>>>> I have a working test but I had to add some docs to the test
data and have tried to reverse engineer the value for k (desiredRank). I came up with the
following but I think it is only an accident that it works.
>>>>>>> int p = 15; //default value for CLI
>>>>>>> int desiredRank = sampleData.size() - p - 1;//number of docs
- p - 1, ?????? not sure why this works
>>>>>>> This seems likely to be an issue only because of the very small
data set and the relationship of rows to columns to p to k. But for the purposes of creating
a test if someone (Dmitriy?) could tell me how to calculate a reasonable p and k from the
dimensions of the tiny data set it would help.
>>>>>>> This test is derived from a non-active SVD test but I'd be up
for cleaning it up and including it as an example in the working but non-active tests. I also
fixed a couple trivial bugs in the non-active Lanczos tests for what it's worth.
>>>>>>> On Aug 9, 2012, at 4:47 PM, Dmitriy Lyubimov <>
>>>>>>> Reading "overview and usage" doc linked on that page
>>>>>>> should help to clarify outputs and usage.
>>>>>>> On Thu, Aug 9, 2012 at 4:44 PM, Dmitriy Lyubimov <>
>>>>>>>> On Thu, Aug 9, 2012 at 4:34 PM, Pat Ferrel <>
>>>>>>>>> Quoth Grant Ingersoll:
>>>>>>>>>> To put this in bin/mahout speak, this would look
like, munging some names and taking liberties with the actual argument to be passed in:
>>>>>>>>>> bin/mahout svd (original -> svdOut)
>>>>>>>>>> bin/mahout cleansvd ...
>>>>>>>>>> bin/mahout transpose svdOut -> svdT
>>>>>>>>>> bin/mahout transpose original -> originalT
>>>>>>>>>> bin/mahout matrixmult originalT svdT -> newMatrix
>>>>>>>>>> bin/mahout kmeans newMatrix
>>>>>>>>> I'm trying to create a test case from testKmeansDSVD2
to use SSVDSolver. Does SSVD require the EigenVerificationJob to clean the eigen vectors?
>>>>>>>> No
>>>>>>>>> if so where does SSVD put the equivalent of DistributedLanczosSolver.RAW_EIGENVECTORS?
Seems like they should be in V* but SSVD creates V so should I transpose V* then run it through
the EigenVerificationJob?
>>>>>>>> no
>>>>>>>> SSVD is SVD, meaning it produces U and V with no further
need to clean that
>>>>>>>>> I get errors when I do so trying to figure out if I'm
on the wrong track.

View raw message