lance, apropos to your comment about not finding documentation on eval-table, there is a combo parser for Rmoses because i didn't know it existed when i was writing the code :P feel free to ask questions about it here or the moses channel in OpenCog slack if you do slack.
On Monday, February 10, 2020 at 5:14:58 PM UTC+8, Lance White wrote: > > Thanks Michael, I am not fluent in R but can hack my way through for > sure! Really great to have this option. I had downloaded it, but did not > realise it can evaluate too. Will definitely spin it up and explore > functionality. > > On Monday, 10 February 2020 14:53:06 UTC+11, Michael Duncan wrote: >> >> if you are familiar with R you can use the wrapper code i wrote for >> binary classification problems: https://github.com/mjsduncan/Rmoses >> >> the documentation is crappy and it needs to be rewritten in the tidyverse >> idiom but it handles producing and scoring combos on training and testing >> partitions and producing feature counts from model ensembles. >> >> it includes a combo parser so you can try the combos on new samples. >> >> On Monday, February 10, 2020 at 5:30:19 AM UTC+8, linas wrote: >>> >>> >>> >>> On Sun, Feb 9, 2020 at 4:26 AM Lance White <[email protected]> wrote: >>> >>>> Hi to All, >>>> >>>> A really basic question about Moses. So I can run the examples and >>>> test files no problems. But how do I use the output combo program? >>>> >>>> moses -H it -i disjunction.csv >>>> >>>> 0 or($1 $2 $3) >>>> >>>> -1 true >>>> >>>> -1 or($1 $2) >>>> >>>> >>>> Let's say I want to use the following on a data set how do I feed it >>>> values and get a result output by moses? >>>> >>>> >>>> or($1 $2 $3) >>>> >>> >>> Well, if you have three values, each being either true or false, well, >>> just pipe those values through the logical or function, and you're done. >>> >>> Yes, this sounds silly, doesn't it? Every user of moses has built some >>> large, complex system around it to feed it tables of data and then to >>> process other data using these outputs. Unfortunately, all of these are >>> proprietary systems, but it occurs to me that maybe one of these could be >>> open-sourced. I'll have to ask. The one I know best can take structured and >>> unstructured data from a variety of sources, filter and process these into >>> training-data sets, collect up a number of best-fit moses colbo results, >>> average them together into an ensemble, and then output that as a >>> data-processing pipeline: you feed it tables (or individual lines from >>> tables) and it makes predictions based on that input. Practical experience >>> shows that it is more-or-less comparable to "decision forests" (a decision >>> forest being an ensemble of decision trees; a decision tree being >>> kind-of-like a combo program but obtained from different algorithms). Both >>> moses and decision-forests max out at a certain level of accuracy, beyond >>> with it takes an ungodly amount of training time to improve on. There was >>> a nascent effort to apply deep learning techniques, but it foundered on a >>> lack of funding. >>> >>> I'm not sure I answered your question, but that's what I've got. >>> >>> -- Linas >>> >>> -- >>> cassette tapes - analog TV - film cameras - you >>> >> -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/63b11d78-620f-4777-9791-7bd81e04799e%40googlegroups.com.
