ChanServ changed the topic of #mlpack to: "mlpack: a fast, flexible machine learning library :: We don't always respond instantly, but we will respond; please be patient :: Logs at http://www.mlpack.org/irc/
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< jonpsy[m]> zoq: About the remote server, will it retain the files and dev-env setup? Also, who all have access to that server ? Because I might git-login there and it might be "not secure" if anyone other than us 3 have access to the server. Or perhaps there is a better way to go about this, let me know :)
< jonpsy[m]> oh ..and also, can i know the specs of the system, just excited :D
< zoq> jonpsy[m]: All files will be retained, yes; but probably not advisable to store secure information on the system, so I would not store e.g. the git credentials.
< jonpsy[m]> cool, about the specs.......?
< jonpsy[m]> btw sent the pub key in the doc 👍️
< zoq> jonpsy[m]: Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz, 64GB RAM
< jonpsy[m]> Yoooooooooooooooo
< jonpsy[m]> sweet!
< zoq> jonpsy[m]: Will create the account tomorrow.
< zoq> jonpsy[m]: I guess we have to see if the latency is good enough for you.
< zoq> Hopefully it's not to bad.
< jonpsy[m]> Ah yes! If the server is in U.S that'd be problem
< jonpsy[m]> could you try banglore server?
< zoq> Germany.
< zoq> Canada might work as well.
< jonpsy[m]> canada is too far :)
< zoq> I'll setup the account, and we will see if it works, I guess.
< jonpsy[m]> is banglore server out of question...?
< jonpsy[m]> i suppose you'll have to pay again for it, correct?
< zoq> I don't have a machine in banglore :(
< jonpsy[m]> Bummer
< zoq> In this case the machine is a build node we use for jenkins as well, it's currently not really used.
< jonpsy[m]> Hmm, i mean if the training is fast (which shouldnt have to do with my latency), i can cope up with it.
< jonpsy[m]> germany works
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< HarshVardhanKuma> Hi guys. I'm having trouble with decision trees cli binding. It seems to reject the .bin file input (which I generated by loading a CSV file in armadillo as a matrix and then saving it). The same bin input file works with other mlpack cli bindings like random_forests and adaboost.
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< HarshVardhanKuma> Hi guys, there is one more doubt regarding mlpack_adaboost cli binding. It does not expose any option to choose the number of estimators - rather there is an option for the number of iterations. I suppose both are equivalent?
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< swaingotnochill[> zoq is there any way to store the dataset I will be working on server..
< swaingotnochill[> (edited) ... I will be working on server.. => ... I am working on server..?
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< zoq> swaingotnochill[: Which dataset are we talking?
< swaingotnochill[> I want to upload the California housing price dataset
< zoq> Ahh, there is no automated process for that, if you can post the link here, pretty sure @rcrutin can upload the dataset for you.
< swaingotnochill[> Okay...then let me just finish the first project..I will then ask Ryan to upload the dataset
< rcurtin[m]> yep, that's easy for me to do, but I don't have an automated script to do it... just send a URL and I'll add it to datasets.tar.gz :)
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< rcurtin[m]> hey, finally, successful docker matrix build: http://ci.mlpack.org/job/mlpack%20docker%20nightly%20build/48/ 😃
< rcurtin[m]> zoq: I'm looking at the `Select` layer right now... the comment is that it "selects the specified column from a given input matrix"---but in the ANN toolkit, a column is a single point in a batch. so is the idea here that we are reducing a batch to a single point? or should instead this be selecting a single dimension (or set of dimensions) from each input point?
< rcurtin[m]> I couldn't find any uses of the `Select` layer in the mlpack codebase, so I wasn't sure; maybe there are some uses in the examples or models repository though
< zoq> It will select a specific dimension, but honestly if there is no test for it, I would say let's remove the layer.
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< rcurtin[m]> oh, it is tested, but just as a standalone layer, not in the context of a larger network
< rcurtin[m]> I'll adapt it to use a specific dimension instead of a column 👍️
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< zoq> rcurtin[m]: looks like #49 is red because of some connection issue
< rcurtin[m]> yeah, I saw that... transient DNS failure on dealgood it seems like?
< rcurtin[m]> I'll watch and see if it keeps happening
< zoq> wondering if it makes sense to print out a warning and continue, because stb is optinal anyway
< rcurtin[m]> yeah, actually the better question is why STB isn't being detected on the system... I installed `libstb-dev`
< zoq> ohh you did, in this case yes that is the better question
< swaingotnochill[> zoq is there any way to read both the categorical and numerical data at same time?
< swaingotnochill[> zoq is there any way to read both the numerical and categorical data at the same time? the data::Load() only loads the numerical data.
< rcurtin[m]> zoq: oh! of course. I am running all this inside a docker container. so I just need to rebuild all the containers with stb inside them...
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< HarshVardhanKuma> I wanted to check if mlpack bindings have any option to run on multicores. I could not find any cli options for number of jobs. I wanted to know if it was possible?
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< rcurtin[m]> HarshVardhanKumar (Harsh Kumar): if you compile with OpenMP, or if you are using OpenBLAS, this will happen automatically (depending on the algorithm) :)
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< rcurtin[m]> also I saw your other messages... can you open an issue for the .bin problem you had with `mlpack_decision_tree`? and you are right that the number of iterations is the number of estimators for AdaBoost 👍️
< zoq> swaingotnochill[: You can read numerical and categorical data at the same time.
< jonpsy[m]> @zoq are you free rn?
< zoq> this will print the numerical data and the categorical data as well
< zoq> jonpsy[m]: Sure
< swaingotnochill[> Thanks
< swaingotnochill[> One more thing to add @zoq
< swaingotnochill[> (edited) ... add @zoq => ... add zoq
< swaingotnochill[> Should I make a seperate notebook for cleaning and EDT of data and the actual algorithm?
< swaingotnochill[> Coz I read in the readme that the idea is to make the examples as short as possible.
< swaingotnochill[> (edited) ... and the actual algorithm? => ... and actual algorithm training?
< zoq> I would put it into a single notebook, would be confusing to split it up, if I e.g. have to run anther notebook to clean the data.
< jonpsy[m]> @zoq So i was thinking on starting with MOEA/D-DE first rather than SPEA-II
< jonpsy[m]> because a good amount of work is already done.
< zoq> jonpsy[m]: yes, was about to say that
< jonpsy[m]> Reading the paper gave me a shocking insight
< jonpsy[m]> wait, i'll just show you
< zoq> looks like you already digged into it quite a bit
< jonpsy[m]> PS => Pareto Set (i.e. in the variable space)
< jonpsy[m]> lemme know what you think abt it
< zoq> I mean it's true that the ZDT functions are simpler than the real world problems, but I think for benchmarking it's still relevant.
< jonpsy[m]> its not just ZDT
< jonpsy[m]> basically, the test-suites we've been building so far focus excessively of PF
< jonpsy[m]> * basically, the test-suites we've been building so far focus excessively on PF
< jonpsy[m]> bt their PS are way too simple, this is usually never the case in real life. (see second pic)
< jonpsy[m]> > basically, the test-suites we've been building so far focus excessively on PF
< jonpsy[m]> by testing various complicated shapes like : Convex, concave, multimodal, discontnuous
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< zoq> Sure, but I don't see the problem, we included them mainly for the purpose of using them inside the test suite.
< jonpsy[m]> i mean yeah
< zoq> No user will use ZDT to solve anything.
< jonpsy[m]> what im stressing at is "they're not enough"
< jonpsy[m]> > No user will use ZDT to solve anything.
< jonpsy[m]> yeah, same point.
< jonpsy[m]> so, these guys proposed a test-suite with complicated Pareto Sets .
< zoq> Don't mind to add some for further testing.
< zoq> If it dosn't take too long to optimize I'm fine with that.
< jonpsy[m]> yeah sure, but mainly, I was taken aback by ZDT PS being this simple.
< jonpsy[m]> that's what i wanted to point out
< zoq> Wondering if they just say that to make MOEA better.
< jonpsy[m]> hahaha, no. Their theory is sound.
< jonpsy[m]> aight, gotta jump, supper. Later then
< zoq> Also, the rocket problem is a good example, it's soemthing that is used in the real world.
< zoq> And I think this is something people like to see.
< zoq> ZDT is really abstract.
< zoq> That's why I like the rocket problem, it has a connection to the real world :)
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< jonpsy[m]> Yep, I plan to tackle MOEA/D-DE and rocket before first eval.
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< swaingotnochill[> Any idea why this error is popping up
< swaingotnochill[> ```error: use of undeclared identifier 'LinearRegression'```
< swaingotnochill[> I m just using LinearRegression<> lr( trainSet, trainLabels, 0.0);
< swaingotnochill[> zoq
< swaingotnochill[> Am i missing something?
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< zoq> let me know if that solves the issue
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< zoq> swaingotnochill[: https://gist.github.com/zoq/5b2ea05ad0719d5a95e4a14b0aa2c09d minimal example
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< swaingotnochill[> Ohh I see....I will make sure to check the methods file to make sure before using them..my error 😅
< swaingotnochill[> Yup...I didn't read the file...thought all method were implemented in same pattern...now when I see, its a class.
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