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] > > >
