mlpack IRC logs, 2017-03-17

Logs for the day 2017-03-17 (starts at 0:00 UTC) are shown below.

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--- Log opened Fri Mar 17 00:00:45 2017
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01:50 < zoq> kris1: There is an issue for that: https://github.com/mlpack/mlpack/pull/487 and in addition someone could just use typeid(object).name() or something similar as a simple indentifier.
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01:59 < mikeling> rcurtin: ping
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02:30 < rcurtin> mikeling: you have to leave a message, it's not likely that I can help in real-time :)
02:31 < rcurtin> but I am here for a few minutes now :)
02:32 < mikeling> rcurtin: ok, I will leave a message on the GitHub. I jut wondering the relateship between weight, label and number of classes
02:32 < mikeling> Here is the progress I have https://github.com/mlpack/mlpack/compare/master...MikeLing:issue-fix882?expand=1
02:33 < mikeling> for quick , I just want to make the weight have the same length of classes like if we have two classes of data
02:34 < mikeling> we only have two kind of weights, each classes have one weight
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02:35 < mikeling> actually, I just think it's more like "if we have two kinds of label, we want two weight for it, each **kind** of label have its correspond weight"
02:36 < rcurtin> no, the weights are for each point not for each class
02:36 < rcurtin> so weights.n_elem should be equal to data.n_elem
02:36 < rcurtin> (not numClasses)
02:36 < rcurtin> does that make sense? so the idea is that each individual observation has some weight, not that each class is weighted differently
02:37 < mikeling> so we still need keep the same length between labels and weights?
02:38 < rcurtin> yes, labels.n_elem should be equal to weights.n_elem
02:41 < mikeling> so? what's the meaning of the weight is 0? I mean if some labels' weight is 0 and some labels's weight is not.
02:41 < mikeling> how we calculate the gain
02:42 < mikeling> in decision stump
02:43 < mikeling> we sum up all the weight and each class's weight.
02:43 < rcurtin> if the weight is 0, then the point should not affect the training result at all
02:43 < rcurtin> the weight is the 'influence' or 'importance' of the point
02:44 < mikeling> how about all the weight is 0?
02:44 < mikeling> when we test it
02:44 < mikeling> we set all the weight as zero
02:44 < rcurtin> if every single weight is 0 then I guess the gain will also be 0... it should be equivalent to if there are no points at all
02:45 < mikeling> ah, make sense! all the things seems clear to me now.
02:45 < rcurtin> ok, great :)
02:46 < rcurtin> I am sure there are papers that talk about weighted decision tree learning, but I don't have a link to one or know a specific one
02:46 < rcurtin> if you searched you could probably find one, but it sounds like you have it figured out either way :)
02:47 < mikeling> yep, thank you for your help!
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05:19 < pvskand> how should I test mlpack_linear_regression on the tests that have been written?
05:19 < pvskand> bin/mlpack_test -t linear_regression?
05:23 < pvskand> No worries! I figured it out :)
05:41 < pvskand> How to make the ann module? I am trying make mlpack_ann but it's returning error!
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10:43 < zoq> Has anyone checked the DatasetMapperImputerTest test?
10:55 < zoq> It looks deterministic, so I guess there is some error.
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11:15 < travis-ci> mlpack/mlpack#2061 (master - 96d201e : Tham): The build was broken.
11:15 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/957f4461cc9b...96d201e57a94
11:15 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/212072101
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14:40 < rcurtin> zoq: yeah, maybe this is because of what Tham just committed
14:41 < rcurtin> I guess I have to debug that before releasing mlpack 2.2.0 then... hopefully I can get that all done today
14:49 < zoq> rcurtin: Do you plan to include the updated SGD class as well?
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14:55 < rcurtin> zoq: yeah, I guess I can, it is easy to port
14:56 < rcurtin> I'll have to take a look through the list of all changes between 2.1.1 and now and see which PRs are easy to backport and which aren't
15:00 < zoq> hm, I'm pretty sure it would break the backward compatibility since right now I can't just use SGD as before (e.g. if someone used ``template<typename> class OptimizerType`` somewhere), we could rename the class to SGDType and make an alias.
15:01 < rcurtin> oh, right... in that case, it will be a bunch of extra work to do that, so I will skip that for 2.2.0
15:01 < rcurtin> if we do want to provide that support, it will be easy to backport that as 2.2.1 in the future weeks
15:02 < zoq> rcurtin: I agree
15:03 < vivekp> On top of latest git master, build is failing with some errors in tests/load_save_test.cpp: https://paste.debian.net/922276/
15:04 < vivekp> But I didn't touch that test file.
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15:07 < Trion> rcurtin: can I take this issue? https://github.com/mlpack/mlpack/issues/756
15:08 < zoq> vivekp: Do you have mlpack header files in /usr/local/include?
15:10 < vivekp> zoq: Yes, I do. Not exactly in /usr/local/include but they are there in mlpack folder in that directory
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15:11 < zoq> vivekp: What is the path?
15:12 < vivekp> Not sure, if it's because I'm building just mlpack_test with make -j4 make_test
15:13 < rcurtin> zoq: vivekp: I know what the issue is, I am about to commit a fix
15:13 < rcurtin> give me 5/10 minutes
15:14 < rcurtin> it is confusing to me why the Jenkins build is not failing when this is a pretty obvious issue that should cause a failure...
15:15 < zoq> rcurtin: I'm not sure vivekp has the same issue we talked about, he can't build the code after the latest commit.
15:15 < rcurtin> yes, this one is a separate issue that I just encountered
15:15 < zoq> ah okay
15:15 < rcurtin> because Tham's PR now uses extern template definitions for Load(), now load_impl.hpp is not included
15:16 < rcurtin> but load_impl.hpp contains Lakshya's new overloads for vectors and row vectors
15:16 < rcurtin> so the fix is to move Lakshya's implementations
15:17 < vivekp> zoq: sorry, had to reboot the system (that multi-job make with multiple sessions running wasn't going well)
15:17 < vivekp> zoq: path is /usr/local/include/mlpack
15:17 < rcurtin> fix pushed
15:19 < zoq> vivekp: I think you can run into some issues if you have some outdated header files in /usr/local/include/mlpack but let's see if the latest commit fixes your problem :)
15:20 < vivekp> Thanks, rcurtin. zoq: Initiating build now
15:21 < Nax> rcurtin: (PR#930) should I create PR for jenkins-conf repo to add Dockerfile or not need yet
15:24 < rcurtin> Nax: I would say, right now that Dockerfile is a nice proof-of-concept but it doesn't do anything to get us closer to having many Docker images we can use, so I don't see a need to include it in the jenkins-conf repo right now
15:26 < Nax> Ok thanks!
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15:26 < Nax> rcurtin: I am working on nuget package. Where should I include its files?
15:27 < pvskand> How to make the ann module? I am trying make mlpack_ann but it's returning error!
15:27 < vivekp> Looks like it's going to take sometime. Halfway through so far :)
15:28 < vivekp> I'll report back when it's done
15:30 < zoq> pvskand: There is no executable for the ann code.
15:31 < zoq> Nax: jenkins-conf repository: https://github.com/mlpack/jenkins-conf
15:33 < rcurtin> vivekp: yeah, the tests can take a long time to build
15:33 < pvskand> zoq: how do I test it then?
15:36 < zoq> pvskand: You could run the tests or write your own executable. The reason we don't provide an executable for the ann code is, there is no single model that you could use.
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15:45 < vivekp> rcurtin: build completed successfully. Thanks for the fix.
15:48 < rcurtin> sure, sorry that the build got broken... that's something ideally we should avoid :)
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15:58 < Nax> zoq: Thanks!
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16:42 < travis-ci> mlpack/mlpack#2063 (master - 5148a92 : Ryan Curtin): The build is still failing.
16:42 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/ecef9419f25b...5148a926c63a
16:42 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/212173028
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17:36 < travis-ci> mlpack/mlpack#2064 (master - dcddad1 : Ryan Curtin): The build is still failing.
17:36 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/5148a926c63a...dcddad179391
17:36 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/212194243
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17:50 < travis-ci> mlpack/mlpack#2065 (master - 8bd7f79 : Ryan Curtin): The build is still failing.
17:50 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/dcddad179391...8bd7f7955a01
17:50 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/212199989
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18:05 -benchmark:#mlpack- MLP_BACKWARD (--input_size=50000 --hidden_size=5000 --output_size=100) | None 2.67 (old) => 2.67 (new) => 0.00 (diff) |
18:05 -benchmark:#mlpack- PERCEPTRON (--max_iterations 1000) | iris 0.02 (old) => 0.02 (new) => -0.00 (diff) | oilspill 0.14 (old) => 0.14 (new) => 0.00 (diff) | ecoli 0.03 (old) => 0.03 (new) => -0.00 (diff) |
18:05 -benchmark:#mlpack- MLP_FORWARD (--input_size=50000 --hidden_size=5000 --output_size=100) | None 0.43 (old) => 0.43 (new) => -0.00 (diff) |
18:05 -benchmark:#mlpack- Benchmarks 5 of 5 passed.
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18:45 < travis-ci> mlpack/mlpack#2066 (master - 4cb7f77 : Ryan Curtin): The build is still failing.
18:45 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/8bd7f7955a01...4cb7f77f74d8
18:45 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/212219742
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19:22 < rcurtin> wow, so it turns out there are a huge number of commits to merge for mlpack-2.2.x, I think I am going to have to take some time and figure out the right way to do this
19:22 < rcurtin> cherry-picking commits is a bad idea because it's so easy to miss a few
19:22 < rcurtin> so I guess I will probably not be able to release 2.2.0 today, it may take a little longer
19:23 < rcurtin> plus I have to fix the imputation bug anyway...
19:27 < zoq> rcurtin: Maybe Tham has an idea, I can comment on the issue an ask him what do you think?
19:27 < rcurtin> yeah, sure, go ahead
19:27 < rcurtin> I am trying to debug it anyway because I want to understand boost::spirit :)
19:28 < rcurtin> I think his intention in the end is to replace a lot of the armadillo parsing with boost::spirit, so it would be good if multiple people understood the code
19:29 < rcurtin> actually hang on I think I see the bug
19:29 < zoq> I just glanced over the code and at least for me there are a couple of things that aren't easy to get.
19:29 < zoq> okay :)
19:30 < rcurtin> yes, I think I will also go over the code and try to add some comments
19:30 < rcurtin> and fix the style too, it's very unlike the rest of the mlpack code :)
19:30 < zoq> I agree
19:31 < zoq> but overall a really nice improvement
19:31 < rcurtin> yeah, definitely, a speed boost for loading data is very important since basically every application needs to load data
19:32 < rcurtin> I have a minor concern here, I think I may have discovered that there is some redundant work here that will make the parser scale poorly, but I am not sure yet... if that's the case I'll open an issue and Tham will probably know what to do
19:32 < zoq> Yeah, he is the expert here
19:33 < rcurtin> great... I fixed the first bug and introduced a second one :)
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19:34 < travis-ci> mlpack/mlpack#2067 (master - 177f076 : Ryan Curtin): The build is still failing.
19:34 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/4cb7f77f74d8...177f0769c7b2
19:34 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/212235129
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19:35 < zoq> We could make a challenge out of it, who solves the issue first without introducing a new bug. :)
19:35 < rcurtin> haha
19:40 < rcurtin> ok, I have fixed the bug, but I need to do some refactoring to fix it the "right" way... I should have the fix pushed in less than an hour
19:41 < zoq> okay, so no challenge :)
19:41 < rcurtin> yeah, fortunately, we get to have a quick fix this time :)
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20:04 < pvskand> Does the rann module have any other module's dependency?
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22:05 < rcurtin> the problem runs way deeper than I thought, it may be next week before I am able to sort this out\
22:05 < rcurtin> I did find the bug a while ago, but finding the correct way to fix it in this code is... difficult
22:05 < rcurtin> everything I touch, something else breaks, I think because the design of the DatasetMapper is brittle
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--- Log closed Sat Mar 18 00:00:47 2017