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#dlab logs for 2017-10-28
- Current topic
- A distributed laboratory. Everything which appears here is logged at https://rawl.es/dlab/irc/2017/
- adamfc (Adam Forsythe-Cheasley)
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- rawles (Simon Rawles)
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supertime: Yeah, as usual I kind of hacked out something that worked first and then realised that it's just topological sort, with a twist. Things are working really nicely now and I can go onto the next bit.
There's probably whole Java frameworks for this, but I kind of want to do it myself so I can extend it easier.
supertime: If you had lots of time, what personal coding/ML/etc project would you do?
- supertime (Tim Kovács)
I’d make a rule explorer. When you run XCS it prints out hundreds of rules with many parameters and you stare at them and try to make sense of what it’s done.
I’d make a tool that lets you sort and filter and query and plot them.
How would the rule explorer work, what would be the dialogue between the computer and the user?
Oh, you just answered that.
So what would a query be, 'tell me all the rules that use feature i'?
I’m don’t know what the difference between filters and queries are. Query just sounded like a good word.
But yes to your example. Or ‘show rules with x>5 AND y<10'
That might also be good for ensemble methods in general.
Yeah, some at least. An ensemble of trees for example, since trees are equivalent to rules
Yeah, like if you train a random forest, you know each of the trees are interpretable, but the forest itself can't explain the data as well. It's just too much to take in.
I guess not everyone wants to explain the data.
Yes, the point is to understand the output better.
But besides understanding the data, it helps understand the algorithm and the parameter settings
So you can learn how to tweak it so it gets the kind of rules you want?
Maybe it could automate that process too.
yeah. i kind of learned what to look for in the output, but it’s akward when it’s just in a text file, so the tool would be useful
automating it would be useful
Maybe someone at Keiki's lab could help you with the implementation of that.
When we were looking at the output of our code I remember scrolling through lots of similar-but-not-the-same rules to see whether it had got close. Having a higher-level description of that would have meant we could have used larger populations, I guess.
Though the range of possible solutions to what we were asking it to do were pretty small.
yeah the idea is to replace the scrolling with something better
it could start simple but be extended in lots of ways e.g. computing equivalence between rules would be a nice extra feature
Could the tool capture something that the user learned about the data, parameters, etc, and feed that back into the experiment?
Keiki’s people would find it useful. if you came to japan that could be your official project, if you wanted.
Well I mean more like the user learns something new about the thing they are trying to model, like the rule language or domain has a new equivalence that the XCS didn't take avantage of, or something.
Yeah, I'm interested in tools that let you kind of elaborate your understanding of some domain and use that to simplify or extend the learning process.
XCS problems are typically quite small (in features), but it might help simplify larger problems enough for them to be more tractable.
I guess I'm mixing up the XCS and the exhaustive search now.
I think it could although I don’t have an example in mind. I guess the idea is to make the rule set less of a black box. I’m motivated by getting the box to work better, but it would help with domain understanding too.
I think that's a general problem with the state of data mining at the moment. Domain understanding is just one solution.
they are typically small as it doesn’t scale well
Welcome Joey! What are your spare-time projects at the moment?
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