No, that doesn't describe the change being discussed, since you've copied the discussion about adding an 'offset'. That's orthogonal. You're also suggesting making withMean=True the default, which we don't want. The point is that if this is *explicitly* requested, the scaler shouldn't refuse to subtract the mean from a sparse vector, and fail.
On Wed, Aug 10, 2016 at 8:47 PM, Tobi Bosede <ani.to...@gmail.com> wrote: > Sean, > > I have created a jira; I hope you don't mind that I borrowed your > explanation of "offset". https://issues.apache.org/jira/browse/SPARK-17001 > > So what did you do to standardize your data, if you didn't use > standardScaler? Did you write a udf to subtract mean and divide by standard > deviation? > > Although I know this is not the best approach for something I plan to put in > production, I have been trying to write a udf to turn the sparse vector into > a dense one and apply the udf in withcolumn(). withColumn() complains that > the data is a tuple. I think the issue might be the datatype parameter. The > function returns a vector of doubles but there is no type that would be > adequate for this. > > sparseToDense=udf(lambda data: float(DenseVector([data.toArray()])), > DoubleType()) > denseTrainingRdf=trainingRdfAssemb.withColumn("denseFeatures", > sparseToDense("features")) > > However the function works outside the udf, but I am unable to add an > arbitrary column to the data frame I started out working with. Thoughts? > > denseFeatures=TrainingRdf.select("features").map(lambda data: > DenseVector([data.features.toArray()])) > denseTrainingRdf=trainingRdfAssemb.withColumn("denseFeatures", > denseFeatures) > > Thanks, > Tobi > > > On Wed, Aug 10, 2016 at 1:01 PM, Nick Pentreath <nick.pentre...@gmail.com> > wrote: >> >> Ah right, got it. As you say for storage it helps significantly, but for >> operations I suspect it puts one back in a "dense-like" position. Still, for >> online / mini-batch algorithms it may still be feasible I guess. >> On Wed, 10 Aug 2016 at 19:50, Sean Owen <so...@cloudera.com> wrote: >>> >>> All elements, I think. Imagine a sparse vector 1:3 3:7 which conceptually >>> represents 0 3 0 7. Imagine it also has an offset stored which applies to >>> all elements. If it is -2 then it now represents -2 1 -2 5, but this >>> requires just one extra value to store. It only helps with storage of a >>> shifted sparse vector; iterating still typically requires iterating all >>> elements. >>> >>> Probably, where this would help, the caller can track this offset and >>> even more efficiently apply this knowledge. I remember digging into this in >>> how sparse covariance matrices are computed. It almost but not quite enabled >>> an optimization. >>> >>> >>> On Wed, Aug 10, 2016, 18:10 Nick Pentreath <nick.pentre...@gmail.com> >>> wrote: >>>> >>>> Sean by 'offset' do you mean basically subtracting the mean but only >>>> from the non-zero elements in each row? >>>> On Wed, 10 Aug 2016 at 19:02, Sean Owen <so...@cloudera.com> wrote: >>>>> >>>>> Yeah I had thought the same, that perhaps it's fine to let the >>>>> StandardScaler proceed, if it's explicitly asked to center, rather >>>>> than refuse to. It's not really much more rope to let a user hang >>>>> herself with, and, blocks legitimate usages (we ran into this last >>>>> week and couldn't use StandardScaler as a result). >>>>> >>>>> I'm personally supportive of the change and don't see a JIRA. I think >>>>> you could at least make one. >>>>> >>>>> On Wed, Aug 10, 2016 at 5:57 PM, Tobi Bosede <ani.to...@gmail.com> >>>>> wrote: >>>>> > Thanks Sean, I agree with 100% that the math is math and dense vs >>>>> > sparse is >>>>> > just a matter of representation. I was trying to convince a co-worker >>>>> > of >>>>> > this to no avail. Sending this email was mainly a sanity check. >>>>> > >>>>> > I think having an offset would be a great idea, although I am not >>>>> > sure how >>>>> > to implement this. However, if anything should be done to rectify >>>>> > this >>>>> > issue, it should be done in the standardScaler, not vectorAssembler. >>>>> > There >>>>> > should not be any forcing of vectorAssembler to produce only dense >>>>> > vectors >>>>> > so as to avoid performance problems with data that does not fit in >>>>> > memory. >>>>> > Furthermore, not every machine learning algo requires >>>>> > standardization. >>>>> > Instead, standardScaler should have withmean=True as default and >>>>> > should >>>>> > apply an offset if the vector is sparse, whereas there would be >>>>> > normal >>>>> > subtraction if the vector is dense. This way the default behavior of >>>>> > standardScaler will always be what is generally understood to be >>>>> > standardization, as opposed to people thinking they are standardizing >>>>> > when >>>>> > they actually are not. >>>>> > >>>>> > Can anyone confirm whether there is a jira already? >>>>> > >>>>> > On Wed, Aug 10, 2016 at 10:58 AM, Sean Owen <so...@cloudera.com> >>>>> > wrote: >>>>> >> >>>>> >> Dense vs sparse is just a question of representation, so doesn't >>>>> >> make >>>>> >> an operation on a vector more or less important as a result. You've >>>>> >> identified the reason that subtracting the mean can be undesirable: >>>>> >> a >>>>> >> notionally billion-element sparse vector becomes too big to fit in >>>>> >> memory at once. >>>>> >> >>>>> >> I know this came up as a problem recently (I think there's a JIRA?) >>>>> >> because VectorAssembler will *sometimes* output a small dense vector >>>>> >> and sometimes output a small sparse vector based on how many zeroes >>>>> >> there are. But that's bad because then the StandardScaler can't >>>>> >> process the output at all. You can work on this if you're >>>>> >> interested; >>>>> >> I think the proposal was to be able to force a dense representation >>>>> >> only in VectorAssembler. I don't know if that's the nature of the >>>>> >> problem you're hitting. >>>>> >> >>>>> >> It can be meaningful to only scale the dimension without centering >>>>> >> it, >>>>> >> but it's not the same thing, no. The math is the math. >>>>> >> >>>>> >> This has come up a few times -- it's necessary to center a sparse >>>>> >> vector but prohibitive to do so. One idea I'd toyed with in the past >>>>> >> was to let a sparse vector have an 'offset' value applied to all >>>>> >> elements. That would let you shift all values while preserving a >>>>> >> sparse representation. I'm not sure if it's worth implementing but >>>>> >> would help this case. >>>>> >> >>>>> >> >>>>> >> >>>>> >> >>>>> >> On Wed, Aug 10, 2016 at 4:41 PM, Tobi Bosede <ani.to...@gmail.com> >>>>> >> wrote: >>>>> >> > Hi everyone, >>>>> >> > >>>>> >> > I am doing some standardization using standardScaler on data from >>>>> >> > VectorAssembler which is represented as sparse vectors. I plan to >>>>> >> > fit a >>>>> >> > regularized model. However, standardScaler does not allow the >>>>> >> > mean to >>>>> >> > be >>>>> >> > subtracted from sparse vectors. It will only divide by the >>>>> >> > standard >>>>> >> > deviation, which I understand is to keep the vector sparse. Thus I >>>>> >> > am >>>>> >> > trying >>>>> >> > to convert my sparse vectors into dense vectors, but this may not >>>>> >> > be >>>>> >> > worthwhile. >>>>> >> > >>>>> >> > So my questions are: >>>>> >> > Is subtracting the mean during standardization only important when >>>>> >> > working >>>>> >> > with dense vectors? Does it not matter for sparse vectors? Is just >>>>> >> > dividing >>>>> >> > by the standard deviation with sparse vectors equivalent to also >>>>> >> > dividing by >>>>> >> > standard deviation w and subtracting mean with dense vectors? >>>>> >> > >>>>> >> > Thank you, >>>>> >> > Tobi >>>>> > >>>>> > >>>>> >>>>> --------------------------------------------------------------------- >>>>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>>>> > --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
