Hi Thank you for your reply. Yes, I can get prediction and original features together. My question is how to tie them back to other parts of the data, which was not in LP.
For example, I have a bunch of other dimensions which are not part of features or label. Sorry if this is a stupid question. On Wed, Aug 17, 2016 at 12:57 PM, Yanbo Liang <yblia...@gmail.com> wrote: > MLlib will keep the original dataset during transformation, it just append > new columns to existing DataFrame. That is you can get both prediction > value and original features from the output DataFrame of model.transform. > > Thanks > Yanbo > > 2016-08-16 17:48 GMT-07:00 ayan guha <guha.a...@gmail.com>: > >> Hi >> >> I have a dataset as follows: >> >> DF: >> amount:float >> date_read:date >> meter_number:string >> >> I am trying to predict future amount based on past 3 weeks consumption >> (and a heaps of weather data related to date). >> >> My Labelpoint looks like >> >> label (populated from DF.amount) >> features (populated from a bunch of other stuff) >> >> Model.predict output: >> label >> prediction >> >> Now, I am trying to put together this prediction value back to meter >> number and date_read from original DF? >> >> One way to assume order of records in DF and Model.predict will be >> exactly same and zip two RDDs. But any other (possibly better) solution? >> >> -- >> Best Regards, >> Ayan Guha >> > > -- Best Regards, Ayan Guha
