mlpack IRC logs, 2018-03-30

Logs for the day 2018-03-30 (starts at 0:00 UTC) are shown below.

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--- Log opened Fri Mar 30 00:00:14 2018
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00:07 -!- Topic for #mlpack: http://www.mlpack.org/ -- We don't respond instantly... but we will respond. Give it a few minutes. Or hours. -- Channel logs: http://www.mlpack.org/irc/
00:07 -!- Topic set by naywhayare [~ryan@spoon.lugatgt.org] [Wed May 21 16:24:10 2014]
00:07 [Users #mlpack]
00:07 [ anirudhm ] [ govg ] [ killer_bee[m]] [ petris ] [ sumedhghaisas]
00:07 [ caladrius[m]] [ gtank ] [ lozhnikov ] [ prakhar_code[m]] [ vivekp ]
00:07 [ djhoulihan ] [ jenkins-mlpack] [ nikhilweee ] [ rcurtin ] [ wiking ]
00:07 [ dk97[m] ] [ K4k ] [ Nisha_ ] [ rcurtin_ ] [ zoq ]
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00:07 -!- Channel #mlpack created Tue Oct 11 18:35:40 2011
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03:04 < united_> Does mlpack implement CNNs? I couldn't find them in the documentation. Also, if I want to use computer vision techniques, it would be better if mlpack has the standard networks like alexnet, VGG, Resnet, etc. Is there any way mlpack users can get access to such networks?
03:08 < rcurtin_> united_: yep, CNNs can be found in src/mlpack/methods/ann/, and a tutorial can be found at http://mlpack.org/docs/mlpack-git/doxygen/anntutorial.html
03:08 < rcurtin_> if you'd like to implement one of the standard networks you were thinking of, you could submit it to the models repository for other users: https://github.com/mlpack/models
03:10 < united_> Cool. I will check which models have been implemented and try implementing a new one. Thanks!
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03:19 < rcurtin_> sounds good :)
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08:27 -!- ricklly [~ricklly@2400:dd01:1034:a00:a165:bc55:3ec1:ca62] has joined #mlpack
08:29 < ricklly> hello, I am working on mlpack decision tree benchmark. Can some explain
08:29 < ricklly> pattern = re.compile(br"""(.*?)Required.*?options:""",
08:29 < ricklly> re.VERBOSE | re.MULTILINE | re.DOTALL)
08:29 < ricklly> to me?
08:30 < ricklly> I know it aims to parse the options
08:31 < ricklly> Unfortunately, it can not parse the options after I run it on my shell
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10:34 < zoq> ricklly: You are right, it's used to parse the output, mainly the timing output. The first step is probably to run the command line manually and to check the output, and too adept the parsing step afterwards.
10:36 < sourabhvarshney1> @rcurtin @zoq Can you please help me over the issue that I am not able to create PRs over many other forked repos of mlpack other than original repo?
10:39 < zoq> sourabhvars: Have you tested to fork the repo over the github interface?
10:40 < sourabhvarshney1> I think we can not fork the repo again if we have the original repo as such like in my case I have already forked mlpack/mlpack but I am not able to create PR on manthan/mlpack?
10:41 < zoq> sourabhvars: you could remove the existing one, I guess you have a local copy anyway.
10:47 < sourabhvarshney1> But I think this is not the best solution. Because whenever I need to create PR to any other fork I need to delete that and add a new one. This doesn't sounds good. Looks like there is a privacy issue with this.
10:48 < sourabhvarshney1> @zoq Can we find another solution to it?
10:50 -!- manthan [6725c94b@gateway/web/freenode/ip.103.37.201.75] has quit [Ping timeout: 260 seconds]
10:50 < zoq> sourabhvars: You can test it out, if it works the github settings are fine, and it's a local issue.
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11:01 < sourabhvarshney1> @zoq I tried but it is not working.
11:02 < sourabhvarshney1> I think the problem lies in the personal settings of the repository. Might be the case, but not sure.
11:03 < zoq> sourabhvars: hm, okay, I'll take a closer look later today
11:05 < sourabhvarshney1> Thanks @zoq
11:06 -!- manthan [6725c94b@gateway/web/freenode/ip.103.37.201.75] has joined #mlpack
11:37 < sourabhvarshney1> @manthan for which tests in decision tree test, we need to have pruning test. We need not to have them for all. We can have it for tests like InformationGainManyPoints etc.
11:37 < sourabhvarshney1> Your ideas?
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11:43 < manthan> i was in the process of thinking about tests and I think that testing the pruning on trees formed through different fitness functions for both numeric and categorical types be sufficient
11:44 < manthan> so we will write tests for each fitness function (for both numeric and categorical data)
11:45 < manthan> if for all the pruned tree is small and produces less error than actual tree on validation set then the algorithm can be verified
11:45 < manthan> rcurtin : zoq : sourabhvarshney1 : sounds good?
11:50 < sourabhvarshney1> Sounds good to me. @manthan we can choose a random training dataset and then train the tree. Then check again for a random validation data set.
11:51 < sourabhvarshney1> This we can do for different fitness functions. What say?
11:54 < manthan> ya, one way could be to split the already used dataset into training and validation sets and use those for our purposes
11:55 < sourabhvarshney1> That is also possible.
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13:36 -!- You're now known as rcurtin
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13:56 < ricklly> what's the relationship bettween iris_train.csv and iris_labels.csv?
13:57 < ricklly> why can not we use iris_train.csv and iris_train_labels.csv?
14:00 < zoq> ricklly: iris_labels.csv contains the labels of the test set
14:00 < zoq> iris_train.csv already contains the labels see the last row
14:01 < manthan> zoq : could you have a look at the pruning PR?
14:01 < ricklly> wait
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14:03 < zoq> manthan: We are in the process of releasing mlpack 3.0, so this has to wait at least one more day.
14:04 < manthan> ok sure, thanks :D
14:05 < manthan> just curious to know, does the present master becomes 3.0?
14:06 < ricklly> zoq: I get it. I have check the iris_train.csv downloaded from mlpack.org/dataset. But no labels exists in the iris dataset under the build directory
14:06 < ricklly> waww, 3.0!
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14:09 < ricklly> I think a identifiable file name is important
14:12 < ricklly> zoq: I wonder if there is necessary to make L-BFGS faster?
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14:15 < zoq> ricklly: A faster optimizer is always great to have, if you find something that could be improved, please feel free to open a PR.
14:16 < zoq> manthan: it's pretty much the same, some more fixes going in as well.
14:17 < rcurtin> yeah, in my view the big effort is not so much the code right now but the documentation and making sure that new users can figure out how to quickly use mlpack
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14:23 < manthan> ohh i see, thanks.
14:24 < ricklly> Ok, right! The top priority is sufficient documentation. I really feel bad when I first met MLPack... I have to read the code and understand the logic
14:25 < ricklly> A perfect python notebook tutorial is welcome
14:30 < manthan> I think an open issue for addition of documentation would have drawn some contributions for documentation. That would have perhaps helped in the process
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14:31 < rcurtin> in my opinion it is very very hard to have new contributors write documentation, because they may not fully understand what it is they are documenting
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15:00 < Prabhat-IIT> zoq: Do we have something like std::map or something like std::vector with push_back capabilities in armadillo?
15:05 < rcurtin> Prabhat-IIT: you can use join_rows() or join_cols() or insert_rows() or insert_cols(), but almost always it is better to allocate all the necessary space in the beginning and then fill the elements
15:08 < Prabhat-IIT> rcurtin: Thanks! btw Is it acceptable to use something like std::vector use its push_back capabilities and later convert it into arma::vec through advanced constructor using iterator in mlpack?
15:08 < Prabhat-IIT> or will it have some extra overheads and that's why we don't generally use std::containers
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15:14 < rcurtin> Prabhat-IIT: it depends a lot on the situation, but in general, I would say that it is typically better to find the size of the arma::vec you will need and then fill it
15:14 < rcurtin> but that is not always possible---like I said, it depends on the situation
15:15 < Prabhat-IIT> rcurtin: Thanks, I was facing this issue while implementing something!
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16:21 < manthan> rcurtin : true, i understand the problem with new contributors
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16:36 < travis-ci> mlpack/mlpack#4555 (master - bd91b37 : Ryan Curtin): The build has errored.
16:36 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/25f87ddcb9c1...bd91b3777fc1
16:36 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/360295888
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18:05 < Prabhat-IIT> zoq: you there?
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18:27 < zoq> Prabhat-IIT: yes
18:28 < Prabhat-IIT> zoq: I've added the sphere function test in SAGA as well as made the modifications for SAG but still the sphereFunction test is still failing on Travis and is passing locally
18:29 < Prabhat-IIT> This had happened with me a lot of time I don't know why
18:29 < Prabhat-IIT> That test passes locally but will not pass on Travis
18:29 < zoq> did you use another random seed for each run?
18:30 < Prabhat-IIT> Nopes
18:30 < Prabhat-IIT> I've almost used your code in test
18:30 < Prabhat-IIT> except the code was not complete
18:30 < zoq> see https://github.com/mlpack/mlpack/issues/922
18:31 < Prabhat-IIT> When you tested spherFunction did it passed at your end?
18:31 < zoq> yes
18:32 < Prabhat-IIT> can you take a look at it once? :(
18:32 < zoq> Haven't tested the SAG method, not sure which one fails
18:33 < Prabhat-IIT> SAGA and SAG both
18:33 < Prabhat-IIT> both on sphereFunction
18:33 < Prabhat-IIT> actually you haven't tested the coordinates
18:33 < Prabhat-IIT> just result
18:33 < Prabhat-IIT> result<0.1
18:34 < Prabhat-IIT> but I think coordinates should also be tested
18:34 < zoq> Sure, but this has to wait some time, we are in the process of releasing mlpack 3.0.
18:34 < Prabhat-IIT> yeah, sure :)
18:35 < Prabhat-IIT> btw I've also renewing the PRs which were left in between by my friends here so that they can be merged
18:35 < zoq> great :)
18:36 < Prabhat-IIT> Good luck for 3.0 :)
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20:09 < travis-ci> mlpack/mlpack#4558 (master - 11def98 : Ryan Curtin): The build passed.
20:09 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/bd91b3777fc1...11def98121f6
20:09 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/360392439
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--- Log closed Sat Mar 31 00:00:16 2018