mlpack IRC logs, 2017-03-06

Logs for the day 2017-03-06 (starts at 0:00 UTC) are shown below.

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--- Log opened Mon Mar 06 00:00:02 2017
--- Day changed Mon Mar 06 2017
00:00 < kris1> zoq: thanks. I am new to use of this type of template programming so sorry if doubts seem silly.
00:02 < zoq> kris1: No worries, it takes some time to dive into the codebase, and we are here to help.
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00:02 < rcurtin> kris1: I think I saw earlier that you posted some useful links you'd found to read about template metaprogramming; thanks for doing that, I imagine it is helpful to others to share these links
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00:16 < kris1> rcurtin: Sure, i will post more if i come across something interesting.
00:43 < kris1> zoq: are we not suppose to call templated functions using function<int, int> format right. But here we call it using funciton(int, int ). But you say these are equivalent in above case. can you point to a link that explains this.
00:43 < kris1> Btw the error is resolved.
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01:16 < zoq> kris1: You can do both, but I think in this particular case it's easier to pass an non used parameter to get the data type.
01:16 < zoq> kris1: Here is an easy example: template<typename T> void test() { T y; } we have to use test<int>(), but we could also do: void template<typename T> test(T x) in this case we do test(x) where x is int x;
01:17 < zoq> kris1: You can also search for "Template argument deduction".
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03:00 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/208053365
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08:58 < thyrix> Hi, is there a description of datasets used in mlpack_test and the benchmark system?
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13:35 < rcurtin> thyrix: they are typically UCI repository datasets or randomly generated datasets
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14:55 < damien> rcurtin, hello. I have a question. Can we build a machine learning based system that monitors the data from 3 sources and identifies if any of them exceed or trigger a value? I'm relatively new to programming and the machinelearning irc wasn't really helpful.
14:55 < damien> I've been using mlpack so i thought i'll ask here.
14:57 < damien> the sources should be synchronous
14:58 < govg> damien: Do you mean like you have three sensors, and you just need to check when any of them exceed a threshold?
14:58 < govg> Or do you wish to learn such a threshold?
14:59 < damien> i have 3 sensors. which give me similar data. not same but similar. So i intend to watch/train them to recognize danger values.
14:59 < govg> Okay, and how is the danger value specified?
14:59 < damien> I'm not sure about making them learn sucha threashold , the application i wish to build would be critical so i think it would be wise to let it know beforehand.. what do you think?
15:00 < damien> Probably numerical values if thats what you're asking.
15:00 < govg> (if you know the threshold beforehand, can't you simply check if the values exceed the threshold at each time step?)
15:02 < damien> exactly right. That's what i thought too. That we can use a deterministic approach.
15:03 < damien> But my college said that a machine learning would be a better approach.
15:04 < damien> saying that if the data is in sync machine learning would be better else if the data is not in sync we can use deterministic approach.
15:05 < damien> I'm not an expert so I'd like to know how to process this.
15:05 < damien> colleague *
15:06 < rcurtin> so, if you're going to do machine learning to learn a threshold, you would need to have training data
15:06 < rcurtin> where basically you have many observations of these sensor values
15:06 < rcurtin> and for each of these observations, you know whether or not you want to issue a warning
15:07 < rcurtin> if you can't obtain data of this type, then doing machine learning is going to be significantly more difficult, and I would say setting a manual threshold would be a better thing
15:07 < rcurtin> if you *can* obtain data of that type, then I would say that you should do something simple like, e.g., logistic regression
15:08 < rcurtin> where you train the logistic regression classifier on your [sensor values, labels] data (where labels are "warning" or "not warning", or whatever is right for your situation)
15:08 < damien> supervised learning?
15:08 < rcurtin> and then you can simply use that logistic regression classifier with your data from the 3 sources at each time step, and it will predict whether or not you should issue a warning
15:08 < rcurtin> yes, exactly, supervised learning is what I'm suggesting here
15:08 < rcurtin> it's possible to do unsupervised learning here, but based on what I am guessing of your situation here, I might suggest that it would be better to use a manually set threshold instead of doing unsupervised learning
15:09 < rcurtin> whether or not that's actually true... well, not sure, you would be able to know best since you know your situation, but that would at least be my off-the-cuff advice :)
15:11 < damien> I agree. I don't want to go with unsupervised learning either. I do have data which i could use to train it.
15:11 < damien> So do you think this is a better approach that the deterministic way?
15:11 < thyrix> How dose your data look like?
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15:13 < damien> thyrix, I didn't get you. I haven't seen the data yet but it is there. So i don't know the format.
15:13 < damien> Its mostly tags and integer values if thats what you mean.
15:14 < rcurtin> if you have a bunch of data for training that is labeled, I think that logistic regression would probably be a better way to go
15:15 < rcurtin> although, if you really want to know, what I would do is build an ROC to evaluate the performance of the logistic regression classifier
15:15 < rcurtin> (unfortunately mlpack has no support for that, you'd have to either implement it manually or probably it's easy enough to use scikit's functionality to do it)
15:15 < rcurtin> and you could also build an ROC for the deterministic threshold classifier you suggested, and you could compare the performance
15:16 < damien> ROC?
15:17 < damien> Oh yes yes. That would be really good.
15:18 < rcurtin> yeah, those are good measures of how your system will perform in practice
15:19 < rcurtin> (under the assumption that the real-world data you'll be getting matches the distribution of the test data you build the ROC with)
15:21 < damien> Yeah so basically the data that i'll be using is previously collected real world data.
15:22 < thyrix> Hi rcurtin: I encountered some issue on the test system, have you see that?
15:23 < damien> But the one thing thats bothering me is, how will the system react to spike values? Like if i were to train the data with low medium values and once i set it in the real world i get super rare high values will the system deal with it?
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15:33 < thyrix> generally, if you noticed the spike values, it's better to delete them(or take a value you prefer)before you feed the data to your algorithm
15:35 < Upendra> hi i want to know about gsoc project you offer??
15:37 < damien> thyrix, that would be a bad decision because as i said it is a critical system and every single value is important especially the high ones.
15:38 < zoq> Upendra: Hello, have you seen: https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas and mlpack.org/gsoc.html?
15:45 < thyrix> damien, how you deal with the data will lead to different model. Sometimes it will work well, but it really depends on the data
15:46 < thyrix> Some experiments are necessary
15:48 < rcurtin> damien: you'd need to have some test data with very large values already to see how the system will behave
15:49 < rcurtin> if you can't get much of that data, then maybe the threshold idea is better
15:51 < damien> thyrix, Makes sense.. I'll give some thought to this.
15:51 < rcurtin> I once did a project where we were trying to detect whether or not chickens were stressed based on the noises they were making
15:52 < rcurtin> so we set up microphones in the growout house, and got tons and tons of data in the normal state where they were not stressed
15:52 < rcurtin> then, we modified the environment several times in a way that would stress them
15:52 < rcurtin> but, this gave us only a few instances where they were stressed out
15:52 < rcurtin> so in the end, we were not able to use a sophisticated classifier, but instead we had to use a simple threshold
15:52 < rcurtin> because not enough was known about the ways that chickens would actually react and sound under stress
15:52 < rcurtin> it sounds like, maybe in your case, it is a similar situation
15:53 < rcurtin> (that was a strange project, but I learned a lot about chickens)
15:55 < thyrix> really interesting :)
15:56 < damien> rcurtin, Yes.
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15:58 < damien> The data set sounds like what i have. I shall ponder on your words. And thanks a ton for explaining it so much. This saved me spending 2 nights trying to understand the web.
16:00 < damien> Because, the high values are rare. Your experience gave me a question, you had a very large sample of non-stressed data, and you had a very small size of stressed data. This does affect the working of the system doesn't it?
16:02 < damien> And yeah thats hilarious. XD You'll come to mind the next time i see a chicken coop.
16:03 < rcurtin> (sorry I'm in a meeting, hang on a few minutes)
16:03 < damien> okay.
16:05 < thyrix> If most label are the same, a model just give same result to all data will have a good accuracy, but do not make sense
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16:09 < thyrix> But rare values in feature has different effect
16:13 < thyrix> If some feature has different effect roughly according to (extremely low, normal, extremely high) or some other criterion, maybe code them as (0, 1, 2) well work better
16:13 < damien> thyrix, depends a lot on the dataset and the usage.
16:17 < damien> thyrix, yeah, thats one way to do it, but are you saying it with a machine learning approach or a static approach? Because if its a machine learning way, can you be more elaborate.
16:18 < thyrix> most machine learning algorithm will learn a function on what you give them, what you need to do is to make it batter to learning.
16:21 < thyrix> large value may cause some instability on a numerical result, but we don't know whether it will be this case..
16:22 < thyrix> so I suggest to try some different approach, and take the one worked best
16:24 < thyrix> If your experiment shows a simple threshold is better, then we should use a threshold
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17:10 < rcurtin> damien: yes, we had very few samples of stressed chickens
17:10 < rcurtin> so, giant class imbalance
17:11 < rcurtin> this presents a problem for supervised learning algorithms
17:11 < rcurtin> therefore the threshold idea was easier to apply
17:11 < rcurtin> another idea might be some outlier detection algorithm
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17:11 < rcurtin> but I am not too knowledgeable in that field
17:19 < topology> rcurtin: i think i understand dual-tree KDE now. It is a very straightforward idea. I do, however, have a silly doubt
17:19 < damien> I see the problem. I'll have to check my data set to see how it is. Then for example, if a car is being taught to drive and its shown how to turn left a lot and to turn right a little then the next time its shown a right it will not do it perfectly. This explains the class imbalance right?
17:25 < damien> outlier detection algorithm. yes this could be a solution too. Probably,like you said the best thing is to run an ROC on the different models and check which is most consistent.
17:28 < damien> topology, you could ask your doubt and then wait. maybe someone else will have an answer. :)
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18:03 < rcurtin> damien: yeah, the difficult thing with the ROC is, if you don't have many examples of situations where you need to detect something, then the ROC may not be very descriptive
18:04 < rcurtin> an assumption in the ROC is that the test data reflects the distribution of the real world data, so if the situations you have for your ROC are not an accurate picture of the situations you'll need to detect in real life, then the ROC may be misleading
18:04 < rcurtin> topology: sure, go ahead, ask the question and I can try to answer
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19:47 < travis-ci> mlpack/mlpack#1975 (master - 18680ff : Ryan Curtin): The build is still failing.
19:47 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/d032540e544b...18680ff6193b
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19:52 < travis-ci> mlpack/mlpack#1976 (master - 41fa0b5 : Ryan Curtin): The build is still failing.
19:52 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/18680ff6193b...41fa0b5e71d8
19:52 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/208316185
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19:55 < travis-ci> mlpack/mlpack#1977 (master - 9b7fce8 : Ryan Curtin): The build is still failing.
19:55 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/41fa0b5e71d8...9b7fce811825
19:55 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/208316963
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20:47 < arunreddy> rcurtin:, zoq: A quick question..
20:48 < arunreddy> Considering a policy class variadic template for SGD, can i safely assume that the order of the poilcy types is known and fixed?
20:49 < arunreddy> <UpdatePolicyType, DecayPolicyType> etc..
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21:02 < rcurtin> arunreddy: I'm not sure I fully understand, can you elaborate a little more?
21:32 < arunreddy> rcurtin: I have explained it in the following gist: https://gist.github.com/arunreddy/f3bcd2709788b4bdf6c9b2570b26d5db
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21:32 < rcurtin> ok, sure I will take a look shortly
21:32 < arunreddy> Let me know if it needs more clarity.
21:43 < rcurtin> arunreddy: I don't think that variadic templates are needed for the SGD class
21:44 < rcurtin> I think the meaning of what zoq was talking about is that you could use these for the Train() method in some algorithm
21:44 < rcurtin> e.g.
21:44 < rcurtin> template<template<class, class...> class OptimizerType>
21:44 < rcurtin> void LogisticRegression::Train(OptimizerType<LogisticRegressionFunction, ...>& opt)
21:45 < rcurtin> but for the SGD class itself, you can just provide defaults and you can get the same behavior where the user does not need to specify the template type
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21:52 < travis-ci> mlpack/mlpack#1979 (master - 805b760 : Ryan Curtin): The build is still failing.
21:52 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/9b7fce811825...805b760e3a73
21:52 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/208357418
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23:05 < arunreddy> rcurtin: Ok got it. So the code for the StandardSGD remains the same.
23:05 < arunreddy> Thanks
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23:24 < shihao> rcurtin: my PR https://github.com/mlpack/mlpack/pull/913 build failed and the failure seems due to gmm test.
23:31 < zoq> shihao: Don't worry about the GMM test, checkout: https://github.com/mlpack/mlpack/issues/922 also no need to post the same message in the github issue and the irc channel, we get a notidication once you make a comment.
23:32 < shihao> zoq: Ok, I'm sorry.
23:34 < zoq> shihao: No problem, just wanted to let you know.
23:35 < shihao> zoq: Does gmm stand for Gaussian Mixture Model? If so, I'd like to take a look at this issue and try to solve it since I just learned a lot Gaussian from nbc.
23:38 < zoq> shihao: yes, feel free to take a look at the test, might be fun :)
23:38 < zoq> shihao: Also, as long as the NBC test or a related test that uses the NBC code does not fail you are fine.
23:40 < shihao> zoq: So how can I rebuild my PR if I changed code that is not a part of this PR?
23:48 < zoq> shihao: You can't restart the build for your PR, not sure why you would do that? As I said as long as your test cases are fine and the rest of the code looks good, we merge it in.
23:49 < shihao> zoq: oh, got it. Thanks!
23:51 < zoq> shihao: If your tests fails and you think it's probably because of some bad initialization, we can restart the build for you.
23:53 < shihao> zoq: It's because of other code. No need for rebuild. Thanks :)
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--- Log closed Tue Mar 07 00:00:31 2017