I just sent the pr for multinomial logistic regression.
On Mon, Jan 6, 2014 at 6:26 PM, Michael Kun Yang <[email protected]>wrote: > Thanks, will do. > > > On Mon, Jan 6, 2014 at 6:21 PM, Reynold Xin <[email protected]> wrote: > >> Thanks. Why don't you submit a pr and then we can work on it? >> >> > On Jan 6, 2014, at 6:15 PM, Michael Kun Yang <[email protected]> >> wrote: >> > >> > Hi Hossein, >> > >> > I can still use LabeledPoint with little modification. Currently I >> convert >> > the category into {0, 1} sequence, but I can do the conversion in the >> body >> > of methods or functions. >> > >> > In order to make the code run faster, I try not to use DoubleMatrix >> > abstraction to avoid memory allocation; another reason is that jblas >> has no >> > data structure to handle symmetric matrix addition efficiently. >> > >> > My code is not very pretty because I handle matrix operations manually >> (by >> > indexing). >> > >> > If you think it is ok, I will make a pull request. >> > >> > >> >> On Mon, Jan 6, 2014 at 5:34 PM, Hossein <[email protected]> wrote: >> >> >> >> Hi Michael, >> >> >> >> This sounds great. Would you please send these as a pull request. >> >> Especially if you can make your Newtown method implementation generic >> such >> >> that it can later be used by other algorithms, it would be very >> helpful. >> >> For example, you could add it as another optimization method under >> >> mllib/optimization. >> >> >> >> Was there a particular reason you chose not use LabeledPoint? >> >> >> >> We have some instructions for contributions here: < >> >> >> https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark> >> >> >> >> Thanks, >> >> >> >> --Hossein >> >> >> >> >> >> On Mon, Jan 6, 2014 at 11:33 AM, Michael Kun Yang < >> [email protected] >> >>> wrote: >> >> >> >>> I actually have two versions: >> >>> one is based on gradient descent like the logistic regression on >> mllib. >> >>> the other is based on Newtown iteration, it is not as fast as SGD, >> but we >> >>> can get all the statistics from it like deviance, p-values and fisher >> >> info. >> >>> >> >>> we can get confusion matrix in both versions >> >>> >> >>> the gradient descent version is just a modification of logistic >> >> regression >> >>> with my own implementation. I did not use LabeledPoints class. >> >>> >> >>> >> >>> On Mon, Jan 6, 2014 at 11:13 AM, Evan Sparks <[email protected]> >> >>> wrote: >> >>> >> >>>> Hi Michael, >> >>>> >> >>>> What strategy are you using to train the multinomial classifier? >> >>>> One-vs-all? I've got an optimized version of that method that I've >> been >> >>>> meaning to clean up and commit for a while. In particular, rather >> than >> >>>> shipping a (potentially very big) model with each map task, I ship it >> >>> once >> >>>> before each iteration with a broadcast variable. Perhaps we can >> compare >> >>>> versions and incorporate some of my optimizations into your code? >> >>>> >> >>>> Thanks, >> >>>> Evan >> >>>> >> >>>>>> On Jan 6, 2014, at 10:57 AM, Michael Kun Yang < >> [email protected]> >> >>>>> wrote: >> >>>>> >> >>>>> Hi Spark-ers, >> >>>>> >> >>>>> I implemented a SGD version of multinomial logistic regression based >> >> on >> >>>>> mllib's optimization package. If this classifier is in the future >> >> plan >> >>> of >> >>>>> mllib, I will be happy to contribute my code. >> >>>>> >> >>>>> Cheers >> >> >> > >
