| --- Log opened Fri Apr 05 00:00:22 2019 | ||
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| gf712 | wiking: ping | 10:38 |
|---|---|---|
| @wiking | pong | 10:40 |
| gf712 | wiking: do you know if it is to initialise a vector with specific values in the meta language? | 10:41 |
| gf712 | ie. RealVector c1_param([1,2,3]) | 10:42 |
| gf712 | right now I do this: | 10:42 |
| gf712 | RealVector c1_param(3) | 10:42 |
| gf712 | c1_param[0] = 0.1 | 10:42 |
| gf712 | c1_param[1] = 1 | 10:42 |
| gf712 | .. | 10:42 |
| @wiking | yeah | 10:45 |
| @wiking | i dont think there's a construct for initlizer list | 10:46 |
| gf712 | ok, thanks! | 10:50 |
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| gf712 | HeikoS: ping | 12:44 |
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| gf712 | wiking: ping? | 12:46 |
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| @HeikoS | gf712: hi | 13:05 |
| gf712 | HeikoS: just saw that you reviewed the PR, let me have a look | 13:06 |
| @HeikoS | yes | 13:06 |
| gf712 | I have an issue with clone, I think | 13:06 |
| @HeikoS | we should discuss a few things | 13:06 |
| gf712 | there seems to be a ref count issue | 13:06 |
| @HeikoS | whats the issue? | 13:06 |
| @HeikoS | ah I see | 13:06 |
| gf712 | I get leaks when I clone | 13:06 |
| @HeikoS | well | 13:07 |
| gf712 | in my case the labels ref count isn't increased... | 13:07 |
| gf712 | I think | 13:07 |
| @HeikoS | I am not surprised :) | 13:07 |
| gf712 | the rest I refcount myself, so it doesn't seem to leak | 13:07 |
| @HeikoS | nobody ever checked the clone does things properly with complicated nested objects | 13:07 |
| @HeikoS | does it block you? | 13:07 |
| @HeikoS | or just leak? | 13:07 |
| gf712 | ill try to fix it | 13:07 |
| gf712 | I think its causing issues in python | 13:07 |
| gf712 | but the cpp example works fine | 13:08 |
| gf712 | ill send you a gist | 13:08 |
| @HeikoS | I wouldnt worry about this for now | 13:08 |
| @HeikoS | if it doesnt block | 13:08 |
| @HeikoS | we can fix that later | 13:08 |
| @HeikoS | since the PR is getting out of control otherwise :) | 13:08 |
| gf712 | https://gist.github.com/gf712/5bc697cdd19a32648fbdd12529673922 | 13:09 |
| gf712 | yea I agree | 13:09 |
| gf712 | I just need to be sure if it is an issue with clone | 13:09 |
| gf712 | my Valgrind skills aren't the best :p | 13:09 |
| @HeikoS | it is a messy tool :) | 13:09 |
| gf712 | HeikoS: the list init makes it cleaner | 13:09 |
| gf712 | in the gist example | 13:10 |
| gf712 | but would have to change yet another thing in the meta language... | 13:10 |
| @HeikoS | yes | 13:11 |
| @HeikoS | but can do later | 13:11 |
| @HeikoS | we can just exploit that most langs have list initializers | 13:11 |
| @HeikoS | but it doesnt matter for now | 13:11 |
| @HeikoS | so the "train" thingi made be stubmle a bit | 13:11 |
| @HeikoS | see comments | 13:11 |
| gf712 | the python script I use to validate https://gist.github.com/gf712/c6a98704bae6fa22026f1e03148173ab | 13:11 |
| gf712 | ok, ill see no | 13:12 |
| gf712 | now | 13:12 |
| gf712 | HeikoS: for a class to be used in a factory does it have to be derived from SGObject? | 13:13 |
| @HeikoS | no | 13:13 |
| @HeikoS | gf712: I mean the problem will be the wrap | 13:14 |
| @HeikoS | that doesnt work for non SGObjects | 13:14 |
| gf712 | yea, so it needs to inherit from the counter class? | 13:14 |
| gf712 | but might not need sgobject? | 13:14 |
| @HeikoS | we can specialize the template | 13:14 |
| @HeikoS | or rather define it for SGObject and for others | 13:15 |
| @HeikoS | actually, doesnt it work for non SGObject? | 13:15 |
| @HeikoS | template <class T> | 13:15 |
| @HeikoS | SG_FORCED_INLINE T wrap(const T& value) | 13:15 |
| @HeikoS | { | 13:15 |
| @HeikoS | return value; | 13:15 |
| @HeikoS | } | 13:15 |
| @HeikoS | ah here we go | 13:16 |
| @HeikoS | template <class T> | 13:16 |
| @HeikoS | SG_FORCED_INLINE Some<T> wrap(T* ptr) | 13:16 |
| @HeikoS | { | 13:16 |
| @HeikoS | return Some<T>::from_raw(ptr); | 13:16 |
| @HeikoS | } | 13:16 |
| @HeikoS | this one needs to be simply restricted to pointers that are subclasses of SGO | 13:16 |
| @HeikoS | std::is_base_of< | 13:16 |
| @HeikoS | CSGObject, typename std::remove_pointer<T>::type>::value | 13:16 |
| @HeikoS | something like that | 13:16 |
| @HeikoS | gf712: but actually | 13:17 |
| @HeikoS | this is also lower priority | 13:17 |
| gf712 | but you still need a ref method no? | 13:17 |
| @HeikoS | biggest thing is the API now I would say | 13:17 |
| gf712 | OK, I am open to suggestions, shouldn't be too hard to implement | 13:18 |
| @HeikoS | so did you see my comment about the modular strucutre? | 13:18 |
| gf712 | the core seems to be done, and it can be flexible | 13:18 |
| @HeikoS | I think the final usage should be in the lines of | 13:19 |
| @HeikoS | params = modelselection_instance.learn(features, labels) | 13:19 |
| @HeikoS | params.to_model().train(data, labels) | 13:20 |
| @HeikoS | and scores can be extracted with an observer | 13:20 |
| @HeikoS | the stuff that giovanni is working on | 13:20 |
| gf712 | what does .learn do? | 13:20 |
| @HeikoS | depends on the strategy | 13:20 |
| @HeikoS | in grid-search it has one big loop over all combinations | 13:21 |
| @HeikoS | and inside it calls .evaluate() | 13:21 |
| @HeikoS | keeps track of the returned score | 13:21 |
| @HeikoS | and updates the best-yet score | 13:21 |
| @HeikoS | (for early stopping) | 13:21 |
| @HeikoS | for gradient-search | 13:21 |
| @HeikoS | it would be different, no big loop over parameters | 13:21 |
| gf712 | and params.to_model() is the best model? | 13:22 |
| @HeikoS | yeah | 13:22 |
| @HeikoS | I mean either the .learn() returns the parameters or the instantiated model | 13:22 |
| gf712 | ok, I see! | 13:22 |
| @HeikoS | but the point is | 13:22 |
| @HeikoS | that it should all be fully against abstract APIs | 13:22 |
| @HeikoS | so that we can exchange the scoring criterion (auc, etc), the evaluation (xval, etc), the way new parameters are generated (grid, random, gradient), | 13:23 |
| @HeikoS | check out the old code a bit, it can actually do that | 13:23 |
| @HeikoS | so for example | 13:23 |
| gf712 | ok, but the parameter generator is a separate class that is passed to model selection? | 13:23 |
| @HeikoS | the gradient learning on the marginal likelihood of gps | 13:23 |
| @HeikoS | there criterion=marginalLikelihood, strategy=gradient | 13:24 |
| @HeikoS | No the way it was done is that the modelselection class implements the parameter generator | 13:24 |
| @HeikoS | thus we have "RandomSearchModelSelection", "GridSearchModelSelection", etc | 13:25 |
| @HeikoS | that is not how we need to do it, just saying how it was until now | 13:25 |
| gf712 | I see | 13:25 |
| gf712 | let me think about how I can bring what I have now closer to that | 13:26 |
| gf712 | I had in mind ParameterTree -> GridParameters -> GridSearchEngine | 13:26 |
| gf712 | ParameterTree can hold scalar values | 13:26 |
| @HeikoS | http://www.shogun-toolbox.org/api/latest/classshogun_1_1CModelSelection.html | 13:27 |
| gf712 | GridParameters holds vectors | 13:27 |
| gf712 | and then would have a RandomParameters structure that samples from a distribution? | 13:27 |
| gf712 | the final engine is common | 13:28 |
| @HeikoS | this kind of stuff for me is more background implementation stuff, not really part of the API | 13:28 |
| gf712 | So how does the user build the tree? | 13:28 |
| @HeikoS | but the api is just either I give some grid parameters or I say "please use gradient" or "sample something" | 13:28 |
| @HeikoS | well for grid search the user of course needs to build it | 13:29 |
| gf712 | but also for random search no? | 13:29 |
| @HeikoS | one option is that yes, the other is to just give boundaries for a uniform distribution, or even some other way of specifying a distribution | 13:29 |
| @HeikoS | but that is maybe future stuff | 13:31 |
| @HeikoS | for now we just need to keep in mind that this would have to be a possibility | 13:32 |
| gf712 | OK, just going through the docs quickly to see how it works | 13:33 |
| @HeikoS | main methods are | 13:33 |
| @HeikoS | virtual CParameterCombination * select_model (bool print_state=false)=0 | 13:33 |
| @HeikoS | oh btw the gradient stuff was added later so some of the design doesnt really look clean | 13:34 |
| gf712 | hmm, I'll have to think about how to implement that | 13:34 |
| gf712 | because I am not using pure virtual functions | 13:35 |
| @HeikoS | like this | 13:35 |
| @HeikoS | CGradientModelSelection (CMachineEvaluation *machine_eval, CModelSelectionParameters *model_parameters=NULL) | 13:35 |
| gf712 | because of the way I designed it causes issues | 13:35 |
| @HeikoS | also note the use of "MachineEvaluation" | 13:35 |
| @HeikoS | yeah sure that is not a problem | 13:35 |
| gf712 | because a node can construct further nodes | 13:35 |
| @HeikoS | ah | 13:35 |
| @HeikoS | mmh | 13:35 |
| @HeikoS | well the question is whether modelselection is ever used from a higher level | 13:35 |
| @HeikoS | so that we need to have a base type | 13:36 |
| gf712 | because the whole design is that it mimics the model | 13:36 |
| gf712 | and uses that to checks what params can be added and so on | 13:36 |
| @HeikoS | I see | 13:36 |
| gf712 | I can add a further abstraction | 13:36 |
| @HeikoS | imo it will be important to somehow unify the different model selection approaches | 13:37 |
| @HeikoS | maybe we dont need an abstract base class | 13:37 |
| @HeikoS | but it has to be modular in some sense | 13:37 |
| @HeikoS | I gotta run now, will be back later and can discuss then though | 13:37 |
| @HeikoS | will you be around in an hour? | 13:37 |
| gf712 | yea, I'll be here all afternoon | 13:38 |
| @HeikoS | ok ill try to be back later then :) see you! | 13:38 |
| gf712 | can discuss later | 13:38 |
| gf712 | ok, see you later! | 13:38 |
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| shoaib98libra | Hey Everyone! I just joined this channel so I thought I'll get to know and introduce myself | 15:46 |
| shoaib98libra | My name is Mohammed Shoaib, 3rd year CS student | 15:47 |
| shoaib98libra | You guys can check out my GitHub here: https://github.com/Mohammed-Shoaib | 15:47 |
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| --- Log closed Sat Apr 06 00:00:24 2019 | ||
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