Interesting. i'd probably be interested to try it out.
On Thu, Apr 28, 2011 at 11:31 PM, Stanley Xu <[email protected]> wrote: > Thanks Ted and Lance. And sorry for the jargon. > > For the delay Ted mentioned, we have already considered that, still thanks a > lot for all the detail ideas, they were pretty helpful. > For the parallelized SGD, just found a new paper about using DSGD in matrix > factorization, it's different from logistic regression, but might helpful as > well. Put the title here "Large-Scale Matrix Factorization with Distributed > Stochastic Gradient Descent" if anyone is interested. > > Best wishes, > Stanley Xu > On Fri, Apr 29, 2011 at 2:08 PM, Ted Dunning <[email protected]> wrote: > >> Yes. >> >> Apologies for jargon and TLA< >> http://en.wikipedia.org/wiki/Three-letter_acronym> >> 's >> >> On Thu, Apr 28, 2011 at 7:04 PM, Lance Norskog <[email protected]> wrote: >> >> > CTR == Clickthrough Rate >> > >> > On Thu, Apr 28, 2011 at 12:06 PM, Ted Dunning <[email protected]> >> > wrote: >> > > On Tue, Apr 26, 2011 at 8:00 PM, Stanley Xu <[email protected]> >> wrote: >> > > >> > >> ... I understood as the algorithm, the time in training only relies on >> > the >> > >> non-zero records, but per our test, there would be some overhead we >> > could >> > >> not ignore for thoso non-zero records, though the cost is sub-linear >> or >> > >> logit to the length of the hashed vector. >> > >> >> > > >> > > This is pretty close if we say "non-zero values". A record usually >> > refers >> > > to an entire training >> > > example. >> > > >> > > The extra work refers mostly to deferred regularization that eventually >> > has >> > > to be >> > > applied. My guess is that it is even less than log in the feature >> vector >> > > size. >> > > >> > > >> > >> And in CTR prediction, I am not pretty sure it will converge very >> > quickly. >> > >> >> > > >> > > I was saying this purely based on the number of features. >> > > >> > > >> > >> Because we will very possibly see some records has the almost same >> > feature >> > >> but different result in display ads. >> > > >> > > >> > > The algorithm can still converge to an estimate of the probability >> here. >> > > >> > > >> > >> But we will see the result in the >> > >> future. >> > > >> > > >> > > You have to be *very* careful about this to avoid prejudicing the model >> > > against >> > > recent impressions. If you have a fast feedback to the ad targeting >> > system, >> > > you >> > > can have severely instability. >> > > >> > > The key thing that you have to do to avoid these biases is to define a >> > > maximum >> > > delay before click for the purposes of modeling. You need to ignore >> all >> > > impressions >> > > younger than this delay (because they may still get a click) and you >> need >> > to >> > > ignore >> > > all clicks after this delay (to avoid bias in favor of old >> impressions). >> > > For on-line ads >> > > you can probably use a maximum delay of a few minutes because most >> clicks >> > > will >> > > happen by then. >> > > >> > > To find a good value for maximum delay, you should plot the CTR for a >> > bunch >> > > of >> > > ads versus delay. This will increase rapidly shortly after zero delay, >> > but >> > > then will >> > > level off. The ordering of ads by CTR is what you care about so you >> can >> > > follow the >> > > curves back and find the shortest delay where the ordering is clearly >> > > preserved. Use >> > > that as your maximum delay. Typically this is roughly where your CTR >> is >> > at >> > > about >> > > 80-90% of the final value. >> > > >> > > >> > > >> > > >> > >> (We were still working on creating a framework to digg all the >> > >> features we need from the log, I would like to share our experience by >> > >> using >> > >> Mahout SGD once we got our CTR prediction model release.) >> > >> >> > >> And for parallelize SGD, what do you mean for help with sparse inputs >> > that >> > >> exhibit long-tail frequency distribution? Would you like to share some >> > of >> > >> your ideas, Ted? >> > >> >> > >> Currently, what I could think about is split the data to multiple >> mapper >> > >> randomly and let every mapper to learn from the local data and get an >> > >> average on the whole model, or let multiple model to vote for every >> > >> feature's weight. A little like the idea of AdaBoost or RandomForest. >> > But I >> > >> am not a scientist or mathematician, so no idea if it is correct or >> not. >> > >> >> > >> >> > >> Thanks so much. >> > >> Stanley Xu >> > >> >> > >> >> > >> >> > >> On Tue, Apr 26, 2011 at 11:16 PM, Ted Dunning <[email protected]> >> > >> wrote: >> > >> >> > >> > On Mon, Apr 25, 2011 at 11:46 PM, Stanley Xu <[email protected]> >> > >> wrote: >> > >> > >> > >> > > 1 hour is acceptable, but I guess you misunderstand the data scale >> I >> > >> mean >> > >> > > here. The 900M records didn't mean 900M Bytes, but 900M lines of >> > >> training >> > >> > > set(900M training example.). If every training data has 1000 >> > dimension, >> > >> > it >> > >> > > means 900 million X 1000 X 16 B = 14TB. If we reduce the logs >> > collected >> > >> > to >> > >> > > 14 days, it would be still 2-3TB data. >> > >> > > >> > >> > >> > >> > Oops. Forgot that last multiplier. >> > >> > >> > >> > >> > >> > > Per our simple test, for 1000 dimension, 10M lines of record, it >> > will >> > >> > take >> > >> > > about 1-2 hours to do the training, so 90M lines of data will cost >> > at >> > >> > least >> > >> > > 90 hours, is that correct? >> > >> > > >> > >> > >> > >> > 10M x 1000 x 8 = 80 GB. >> > >> > >> > >> > 1-2 hours = (approx) 5000 seconds. So this is >> > >> > >> > >> > 80 GB / 5000 s = 80/5 MB /s = 16MB / s >> > >> > >> > >> > Yes. This is reasonable speed. I think you can get a small factor >> > >> faster >> > >> > than this with SGD. I have seen 100 million records with more >> > non-zero >> > >> > values than you describe with a training time of 3 hours. I would >> not >> > >> > expect even as much as a factor of 10 speedup here. >> > >> > >> > >> > >> > >> > > >> > >> > > And from the PPT you provided >> > >> > > http://www.slideshare.net/tdunning/sdforum-11042010 >> > >> > > You said it would take less than an hour for 20M data records for >> > >> > > numeric/category mixed dimensions. I am wondering, how many >> > dimensions >> > >> > per >> > >> > > record? >> > >> > > >> > >> > >> > >> > These are sparse records records with about a thousand non-zero >> > elements >> > >> > per >> > >> > record. >> > >> > >> > >> > >> > >> > But let's step back to your data for a moment. Where do these >> > thousand >> > >> > dimensions come from? Do you really have a thousand hand-built >> > features? >> > >> > Do you not have any sparse, text-like features? >> > >> > >> > >> > If you really only have a thousand dimensional problem, then I think >> > your >> > >> > model might exhibit early convergence. >> > >> > >> > >> > If not, it is quite possible to parallelize SGD, but this is only >> > likely >> > >> to >> > >> > help with sparse inputs that exhibit long-tail frequency >> distribution. >> > >> > >> > >> >> > > >> > >> > >> > >> > -- >> > Lance Norskog >> > [email protected] >> > >> >
