verne.freenode.net changed the topic of #mlpack to: 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/
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< travis-ci>
ShikharJ/mlpack#77 (ResizeLayer - d00cef6 : Shikhar Jaiswal): The build has errored.
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< Atharva>
How do you run the boost unit tests from terminal in ubuntu? Do I compile the test files in the source code or do I have to do something else?
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< haritha1313>
Atharva: You will have to compile the tests (use command like g++ testname.cpp -o object -std=c++11 -lmlpack -larmadillo -lboost_serialization -lboost_program_options -lboost_unit_test_framework) and run the object file.
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< pawsed>
i ultimately ended up building it on a virtual machine linux
< pawsed>
there should a windows guide too , conda or any other way possible
< pawsed>
should be a *
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< pawsed>
@zoq so after i've built it, are there specific kinds of issues for newcomers?
< zoq>
Atharva: Hello, writing tests is done with the Boost Unit Test Framework. If you make a test suite called "TestSuite" (BOOST_AUTO_TEST_SUITE(TestSuite)), and then build 'mlpack_test' ('make mlpack_test'), you can run only the tests in that test suite with 'bin/mlpack_test -t TestSuite'. A specific test case called 'TestCase' (BOOST_AUTO_TEST_CASE(TestCase)) could be run with 'bin/mlpack_test -t
< zoq>
TestSuite/TestCase'.
< zoq>
pawsed: Take a look at the open issues on Github, also you can always go over the codebase and see if something could be extended or improved. And we are always open for new contributions so if you have something interesting in mind, please feel freeto submit a PR.
< zoq>
pawsed: There are a bunch of interesting variations, in case you like to work on GANs.
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< pawsed_>
does it usually take a couple of hours to build?
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< manish7294>
pawsed: the first full code build may take time max upto an hour but after then you may just build the single component you may be working on.
< zoq>
manish7294 pawsed: Depending on your system you could build with: make -j#numberofcores so in case you have 4 cores 'make -j4' should accelerate the build process.
< kallivad>
Hi all. I need some advice of sparse coding mlpack abilities. Can I build some matrix decomposition with aligned sparse codes? I mean the resulting codes have only for example 10 nonzero codes mainly ( but not already).
< manish7294>
zoq: Thanks for that. It will surely be better to utilise full power of our resources.
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< rcurtin>
kallivad: you could just specify --codes to get 10 codes
< rcurtin>
or do you mean that you need each code to have only 10 nonzero elements?
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< namrata>
Hi, I'm new to the community, and looking forward to contribute to the community. I found the organization via GSoC 2018, and would love to dicuss the VAE project given on the ideas list with the project mentor(s)/community.
< namrata>
More information about me: I'm a final year undergrad student from NSIT, India.
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< sshkhrnwbie>
@zoq : Just came across your blog post "A visualization of machine learning optimizers with the mlpack framework" via r/machinelearning . Must say it is really amazing and great tool for beginners :)
< sshkhrnwbie>
Also I have my research group's meeting on saturday so I wasn't able to follow up on the variance scaling initializer tests and RReLU. plan to do resume on it by monday
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< kallivad>
rcurtin: I mean each code has only 10 nonzero elements
< kallivad>
Not all codes but 99%
< kallivad>
rcurtin: Not all codes but 99%
< kallivad>
rcurtin: Do you have any approaches fo such code structure?
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< rcurtin>
kallivad: that's a pretty significant change to the optimization itself... if you have a paper handy that you are trying to imitate the results of, maybe the existing implementation can be adapted
< desai-aditya>
I am quite new to open source and reading such a huge codebase is overwhelming . I have built mlpack from source and run tests successfully on the stable version. Can someone give me pointers on how to proceed understanding the codebase (I know it's a newbie thing but I sincerely request you to please bear with me , I will prove to be useful in sometime -- sorry) ?
< rcurtin>
kallivad: the best approach I think we have right now is to increase the L1 penalty for optimization to encourage the elements to be more sparse
< rcurtin>
desai-aditya: you are right, it is a large codebase. but probably the easiest way to learn it is to pick one specific machine learning algorithm (say, logistic regression because it is pretty straightforward), and run the CLI program for it
< rcurtin>
once you're comfortable with the CLI program (or the python binding, I guess it doesn't matter which), learn the C++ API for it and when you understand that, start digging into the internals to see how it works
< rcurtin>
as you learn pieces of the mlpack codebase like that, then you can start looking at different algorithms and understanding more
< rcurtin>
but for the most part, the stuff in methods/ is pretty orthogonal and can be understood by itself (assuming you are familiar enough with the core of the library in core/)
< desai-aditya>
@rcurtin : I will do it and report as soon as possible . Thank you very much. I just needed direction. I hope I will soon be able to contribute and hopefully be a part of GSOC18.
< rcurtin>
when you feel that you are ready to contribute, probably the best thing to do is find an open issue that is suitable for your interests
< desai-aditya>
@rcurtin : Is there a threshold for PRs (say 1 or 2) which is required to be selected for GSOC?
< kallivad>
rcurtin: I have no paper, just some codes and basis obtained from some application. I'am trying to understand how this codes have been learned from sourse dataset, generated from this data.
< kallivad>
rcurtin: But thank you for advice. I'll try to encrease L1 reg parameter
< zoq>
desai-adity: No, it's not required.
< desai-aditya>
@zoq : Any tips for beginners like me?
< kallivad>
Another question. How can I use sparse_coding example to do decomposition without dictionary learning step? I mean I have got some dictionary from another clusterization example. sparse_coding needs a model -M file for this. Or I have to change the code by myself?
< zoq>
desai-adity: I would follow rcurtin's suggestion. If you need any help, let us know we are here to help.
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< zoq>
sshkhrnwbie: Great that you liked the visualization :) Also no worries, take all the time you need.
< desai-aditya>
@zoq,rcurtin : Thanks a lot.
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< rcurtin>
kallivad: sorry for the slow response. I think you can create a model by setting --initial_dictionary and --max_iterations = 1 and then saving the output model
< rcurtin>
that might still take one step though
< rcurtin>
another option is to save some output model of the right dimensions as XML, then manually modify the dictionary (that is a little ugly though)
< rcurtin>
yet another option would be to write a simple C++ program that creates a SparseCoding object, sets the dictionary in it, then save that to a file with data::Save(), then you can load it with mlpack_sparse_coding
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< kallivad>
rcurtin: Thank you for this workaround. Now I'll try do do it.
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< rcurtin>
sure, let me know if you have any issues and I can try to help
< rcurtin>
more importantly, if you have any trouble with the documentation, say something so we can improve it :)
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< travis-ci>
mlpack/mlpack#4005 (master - 76115e3 : Ryan Curtin): The build has errored.
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< manish7294>
rcurtin: Thanks for responding on arma::accu() issue. I was not sure to whether take this on the DBSCAN PR, so I took up on IRC. Working of arma::accu() is definitely a point of concern, if we like to use it inplace of loop then checking on it's implementation is a good idea. If you can give me some points to get started with armadillo then it sure, will be helpful. I would try to go through it whenever I would found some spare
< rcurtin>
manish7294: sounds good. I would say that the Armadillo codebase can be quite hard to understand (and worse sourceforge is down right now so the docs aren't available)
< rcurtin>
maybe you can read those and this will be helpful for understanding the structure of Armadillo, if you want to dive in
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< ShikharJ>
rcurtin: What is the standard way of debugging code in mlpack? I was working on the bilinear interpolation layer, and it turns out at some point the code is accessing matrix indices which are out of bounds.
< rcurtin>
I typically compile with debugging symbols (cmake -DDEBUG=ON) and then use gdb to find where the exception is thrown and look at the backtrace and inspect the variable state
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< travis-ci>
mlpack/mlpack#4008 (master - 617a2ac : Ryan Curtin): The build has errored.
< rcurtin>
an update about the build infrastructure: I have the 5 benchmarking systems and masterblaster online... but we still have no external IPs so they are not accessible
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< desai-aditya>
@rcurtin : Can you please suggest a good resource to learn template metaprogramming (I have a basic idea of templates but never used them much) and other programming techniques I'll need to equip myself with?
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< rcurtin>
desai-aditya: I really like Alexei Alexandrescu's book "Modern C++ Design"
< rcurtin>
I keep it on my shelf and refer to it periodically, although the techniques there are a little out of date at this point
< rcurtin>
still, I think it is a good place to start seeing what is possible with template metaprogramming
< ShikharJ>
desai-aditya: I'd second rcurtin's recommendation. "Modern C++ Design" is a classic resource!
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< mlpack>
desai-aditya: You can find some good reference links on mlpack.org/gsoc.html
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< manish7294>
uh-oh somehow my nick got accidently changed to mlpack :)
< rcurtin>
oops :)
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< manish7294>
oh, I didn't see that some good references were already mentioned. Just one another one I would like to add as a good starter is "An idiots guide to C++ templates"
< desai-aditya>
@rcurtin , @ShikharJ : Thanks for your suggestions. What other books will be required to fully understand mlpack?(of course just reading books will never suffice if I don't read code which I am already starting to understand thanks for your previous suggestions rcurtin!)
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< manish7294>
desai-aditya: I think Ryan's suggestions would be good enough for getting started you don't need to study any other book explicitly. You can read over and understand the working of any module you think would be best for you and then try to play with it :) or you can pickup some issue that interests you.
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< desai-aditya>
Thanks a lot manish .
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< Atharva>
I am a little confused about what exactly do the binding tests do, because range_search_main doesn't have any tests yet but still errors are displayed when required.
< manish7294>
atharva: there is range_search_test.cpp in tests.
< manish7294>
You can get some starting from it as how range search works
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< Atharva>
But those aren't tests for bindings right, those are just all the tests for RangeSearch class
< manish7294>
Consequently going through range_search_main.cpp should help
< manish7294>
Ya those are RangeSearch class that but I think they are quite helpful when working on bindings tests
< Atharva>
Yes, but my doubt is if range_search_test already exists in tests, then what would range_search_test in main_tests do?
< Atharva>
okay
< manish7294>
It will test the working of command line and python bindings.
< Atharva>
Okay, thanks
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< manish7294>
As for instance a user may be specify a invalid parameters (like -1 for leaf_size) than program should report the error occurred.
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< travis-ci>
ShikharJ/mlpack#78 (ResizeLayer - 635dac5 : Shikhar Jaiswal): The build has errored.
< Atharva>
manish7294: But even without the binding tests, the program reports the error saying leaf_size should be greater than 0
< manish7294>
That is how it supposed to be. I think you are confusing tests with actual working of code. Tests are for making sure that we get the expected output from the program. Test do not participate in working of the program. They are just for checking correctness of it. Let me know if I can elaborate more.
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< Atharva>
Okay, so we don't run these tests every time the program runs, is that right.
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< ShikharJ>
Atharva: Yes.
< Atharva>
I am sorry if I am sounding stupid here, but if a function or a class is capable of throwing all the errors itself, then what exactly are we testing it for?
< ShikharJ>
The whole concept of testing is that the provided code should initially be correct, and its correctness be maintained even when used with changes made elsewhere in the codebase.
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< Atharva>
okay
< ShikharJ>
The instance of a class or a function throwing an error isn't related to testing, as a class throwing an error would mean that something is wrong with the code used. A testcase failure shows that the applied logic behind the code implementation is at fault.
< Atharva>
Okay, that finally cleared things up for me, thank you!
< ShikharJ>
As for the case where the program says that "leaf_size should be greater than 0", please check the codebase. I'm guessing, it is an assertion inside the function used which is failing.
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< ShikharJ>
Some functions need to have assertions in them, for example a function for multiplying two matrices would need to have an assertion that the number of columns of the first matrix and the number of rows of the second matrix be equal. Otherwise matrix multiplication is not possible. Tests are used to check whether the function actually returned the correct output.
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< pawsed_>
im getting a "error while loading shared libraries error " while trying the comand mlpack_knn --help , what could be the matter?
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< zoq>
pawsed: "error while loading shared libraries: libmlpack.so.2: cannot open shared object file: No such file or directory
< zoq>
that one?
< pawsed>
yeah
< zoq>
set your LD_LIBRARY_PATH
< robertohueso>
I just made my first PR #1253, I hope it's not too bad
< zoq>
e.g. export LD_LIBRARY_PATH="/usr/local/lib/:$LD_LIBRARY_PATH"
< zoq>
/usr/local/lib/ is the path where libmlpack.so is located
< zoq>
so you might have to change it
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< pawsed>
so i change the ld library path to the path of mlpack/build/lib?
< zoq>
yes, but use the full path
< pawsed>
alright that worked thanks a lot
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< pawsed>
oh are there any bugs i can look at for starters ?
< zoq>
Just take a look at the open issues on Github, maybe you find something interesting.
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< Charan>
hello
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< Charan>
I am interested to work for mlpack for gsoc,I have some project proposals in mind, I have emailed mlpack@lists.mlpack.org the proposals, with whom can I discuss them?
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< travis-ci>
PlantsAndBuildings/mlpack#7 (master - 4bd01bb : Marcus Edel): The build has errored.