I'm running a local, single-system test using Qizmt of a simple MapReduce operation. At the end of the 'Map' phase I am calling:
output.Add(rKey, rValue);
This is called let's say a million times, and the keys are 1,2,3,4,5,6 etc - each unique (I'm just testing, after all). I've checked that this is happening as intended. It is. The function is called a million independent times and the values input are correct.
To my surprise, in the 'Reduce' phase only, say, a hundred or so keys are actually received (that is, the 'Reduce' function is called only a hundred or so times). These keys seem to picked almost arbitrarily from the list mapped with a pattern such as the following: 0, 383, 766, 2858, 3241, 5716, etc.
The set of keys which make it through to 'Reduce' are consistent. ie. I will get 0, 383, 766, etc consistently each run of the task. However, if I add a second system to the cluster, the set of keys which make it through is changed entirely!
The stage between 'Map' and 'Reduce' is a bit of a mystery to me - and I'm really not sure where to look since my Map function appears to be behaving exactly as intended (that is, calling 'output' a million times). Does anyone with more experience of parallel processing have any idea where I should be looking, or anything that might cause this behaviour?
Didn't resolve. Ultimately wrote the project in another language and didn't encounter the same issue. Qizmt doesn't seem to be getting updated at all anyway.
Related
Inlet -> front -> middle -> rear -> outlet
Those five properties have a value anything between 4 - 40. Now i want to calculate a specific match for each of those values that is either a full 10 or a 5 when a single property is summed from each pipe piece. There might be hundreds of different pipe pieces all with different properties.
So if i have all 5 pieces and when summed, their properties go like 54,51,23,71,37. That is not good and not what im looking.
Instead 55,50,25,70,40. That would be perfect.
My trouble is there are so many of the pieces that it would be insane to do the miss'matching manually, and new ones come up frequently.
I have manually inserted about 100 of these already into SQLite, but should be easy to convert into any excel or other database formats, so answer can be related to anything like mysql or googlesheets.
I need the calculation that takes every piece in account and results either in "no match" or tells me the id of each piece that is required for a match and if multiple matches are available, it separates them.
Edit: Even just the math needed to do this kind of calculation would be a lot of help here, not much of a math guy myself. I guess there should be a reference piece i need to use and then that gets checked against every possible scenario.
If the value you want to verify is in A1, use: =ROUND(A1/5,0)*5
If the pipes may not be shorter than the given values, use =CEILING(A1,5)
I need to convert thousands of binary byte strings, each about a megabyte long, into ASC strings. This is what I have been doing, and seems too slow:
sub fileToCorrectUTF8Str ($fileName) { # binary file
my $finalString = "";
my $fileBuf = slurp($fileName, :bin);
for #$fileBuf { $finalString = $finalString ~ $_.chr; };
return $finalString;
}
~#b turns #b into string with all elements separated by space, but this is not what I want. If #b = < a b c d >; the ~#b is "a b c d"; but I just want "abcd", and I want to do this REALLY fast.
So, what is the best way? I can't really use hyper for parallelism because the final string is constructed sequentially. Or can I?
TL;DR On an old rakudo, .decode is about 100X times as fast.
In longer form to match your code:
sub fileToCorrectUTF8Str ($fileName) { # binary file
slurp($fileName, :bin).decode
}
Performance notes
First, here's what I wrote for testing:
# Create million and 1 bytes long file:
spurt 'foo', "1234\n6789\n" x 1e5 ~ 'Z', :bin;
# (`say` the last character to check work is done)
say .decode.substr(1e6) with slurp 'foo', :bin;
# fileToCorrectUTF8Str 'foo' );
say now - INIT now;
On TIO.run's 2018.12 rakudo, the above .decode weighs in at about .05 seconds per million byte file instead of about 5 seconds for your solution.
You could/should of course test on your system and/or using later versions of rakudo. I would expect the difference to remain in the same order, but for the absolute times to improve markedly as the years roll by.[1]
Why is it 100X as fast?
Well, first, # on a Buf / Blob explicitly forces raku to view the erstwhile single item (a buffer) as a plural thing (a list of elements aka multiple items). That means high level iteration which, for a million element buffer, is immediately a million high level iterations/operations instead of just one high level operation.
Second, using .decode not only avoids iteration but only incurs relatively slow method call overhead once per file whereas when iterating there are potentially a million .chr calls per file. Method calls are (at least semantically) late-bound which is in principle relatively costly compared to, for example, calling a sub instead of a method (subs are generally early bound).
That all said:
Remember Caveat Empty[1]. For example, rakudo's standard classes generate method caches, and it's plausible the compiler just in-lines the method anyway, so it's possible there is negligible overhead for the method call aspect.
See also the doc's Performance page, especially Use existing high performance code.
Is the Buf.Str error message LTA?
Update See Liz++'s comment.
If you try to use .Str on a Buf or Blob (or equivalent, such as using the ~ prefix on it) you'll get an exception. Currently the message is:
Cannot use a Buf as a string, but you called the Str method on it
The doc for .Str on a Buf/Blob currently says:
In order to convert to a Str you need to use .decode.
It's arguably LTA that the error message doesn't suggest the same thing.
Then again, before deciding what to do about this, if anything, we need to consider what, and how, folk could learn from anything that goes wrong, including signals about it, such as error messages, and also what and how they do in fact currently learn, and bias our reactions toward building the right culture and infrastructure.
In particular, if folk can easily connect between an error message they see, and online discussion that elaborates on it, that needs to be taken into account and perhaps encouraged and/or made easier.
For example, there's now this SO covering this issue with the error message in it, so a google is likely to get someone here. Leaning on that might well be a more appropriate path forward than changing the error message. Or it might not. The change would be easy...
Please consider commenting below and/or searching existing rakudo issues to see if improvement of the Buf.Str error message is being considered and/or whether you wish to open an issue to propose it be altered. Every rock moved is at least great exercise, and, as our collective effort becomes increasingly wise, improves (our view of) the mountain.
Footnotes
[1] As the well known Latin saying Caveat Empty goes, both absolute and relative performance of any particular raku feature, and more generally any particular code, is always subject to variation due to factors including one's system's capabilities, its load during the time it's running the code, and any optimization done by the compiler. Thus, for example, if your system is "empty", then your code may run faster. Or, as another example, if you wait a year or three for the compiler to get faster, advances in rakudo's performance continue to look promising.
I'm not sure what exactly I'm trying to ask. I want to be able to make some code that can easily take an initial and final state and some rules, and determine paths/choices to get there.
So think, for example, in a game like Starcraft. To build a factory I need to have a barracks and a command center already built. So if I have nothing and I want a factory I might say ->Command Center->Barracks->Factory. Each thing takes time and resources, and that should be noted and considered in the path. If I want my factory at 5 minutes there are less options then if I want it at 10.
Also, the engine should be able to calculate available resources and utilize them effectively. Those three buildings might cost 600 total minerals but the engine should plan the Command Center when it would have 200 (or w/e it costs).
This would ultimately have requirements similar to 10 marines # 5 minutes, infantry weapons upgrade at 6:30, 30 marines at 10 minutes, Factory # 11, etc...
So, how do I go about doing something like this? My first thought was to use some procedural language and make all the decisions from the ground up. I could simulate the system and branching and making different choices. Ultimately, some choices are going quickly make it impossible to reach goals later (If I build 20 Supply Depots I'm prob not going to make that factory on time.)
So then I thought weren't functional languages designed for this? I tried to write some prolog but I've been having trouble with stuff like time and distance calculations. And I'm not sure the best way to return the "plan".
I was thinking I could write:
depends_on(factory, barracks)
depends_on(barracks, command_center)
builds_from(marine, barracks)
build_time(command_center, 60)
build_time(barracks, 45)
build_time(factory, 30)
minerals(command_center, 400)
...
build(X) :-
depends_on(X, Y),
build_time(X, T),
minerals(X, M),
...
Here's where I get confused. I'm not sure how to construct this function and a query to get anything even close to what I want. I would have to somehow account for rate at which minerals are gathered during the time spent building and other possible paths with extra gold. If I only want 1 marine in 10 minutes I would want the engine to generate lots of plans because there are lots of ways to end with 1 marine at 10 minutes (maybe cut it off after so many, not sure how you do that in prolog).
I'm looking for advice on how to continue down this path or advice about other options. I haven't been able to find anything more useful than towers of hanoi and ancestry examples for AI so even some good articles explaining how to use prolog to DO REAL THINGS would be amazing. And if I somehow can get these rules set up in a useful way how to I get the "plans" prolog came up with (ways to solve the query) other than writing to stdout like all the towers of hanoi examples do? Or is that the preferred way?
My other question is, my main code is in ruby (and potentially other languages) and the options to communicate with prolog are calling my prolog program from within ruby, accessing a virtual file system from within prolog, or some kind of database structure (unlikely). I'm using SWI-Prolog atm, would I be better off doing this procedurally in Ruby or would constructing this in a functional language like prolog or haskall be worth the extra effort integrating?
I'm sorry if this is unclear, I appreciate any attempt to help, and I'll re-word things that are unclear.
Your question is typical and very common for users of procedural languages who first try Prolog. It is very easy to solve: You need to think in terms of relations between successive states of your world. A state of your world consists for example of the time elapsed, the minerals available, the things you already built etc. Such a state can be easily represented with a Prolog term, and could look for example like time_minerals_buildings(10, 10000, [barracks,factory])). Given such a state, you need to describe what the state's possible successor states look like. For example:
state_successor(State0, State) :-
State0 = time_minerals_buildings(Time0, Minerals0, Buildings0),
Time is Time0 + 1,
can_build_new_building(Buildings0, Building),
building_minerals(Building, MB),
Minerals is Minerals0 - MB,
Minerals >= 0,
State = time_minerals_buildings(Time, Minerals, Building).
I am using the explicit naming convention (State0 -> State) to make clear that we are talking about successive states. You can of course also pull the unifications into the clause head. The example code is purely hypothetical and could look rather different in your final application. In this case, I am describing that the new state's elapsed time is the old state's time + 1, that the new amount of minerals decreases by the amount required to build Building, and that I have a predicate can_build_new_building(Bs, B), which is true when a new building B can be built assuming that the buildings given in Bs are already built. I assume it is a non-deterministic predicate in general, and will yield all possible answers (= new buildings that can be built) on backtracking, and I leave it as an exercise for you to define such a predicate.
Given such a predicate state_successor/2, which relates a state of the world to its direct possible successors, you can easily define a path of states that lead to a desired final state. In its simplest form, it will look similar to the following DCG that describes a list of successive states:
states(State0) -->
( { final_state(State0) } -> []
; [State0],
{ state_successor(State0, State1) },
states(State1)
).
You can then use for example iterative deepening to search for solutions:
?- initial_state(S0), length(Path, _), phrase(states(S0), Path).
Also, you can keep track of states you already considered and avoid re-exploring them etc.
The reason you get confused with the example code you posted is essentially that build/1 does not have enough arguments to describe what you want. You need at least two arguments: One is the current state of the world, and the other is a possible successor to this given state. Given such a relation, everything else you need can be described easily. I hope this answers your question.
Caveat: my Prolog is rusty and shallow, so this may be off base
Perhaps a 'difference engine' approach would be appropriate:
given a goal like 'build factory',
backwards-chaining relations would check for has-barracks and tell you first to build-barracks,
which would check for has-command-center and tell you to build-command-center,
and so on,
accumulating a plan (and costs) along the way
If this is practical, it may be more flexible than a state-based approach... or it may be the same thing wearing a different t-shirt!
Nominally a good problem to have, but I'm pretty sure it is because something funny is going on...
As context, I'm working on a problem in the facial expression/recognition space, so getting 100% accuracy seems incredibly implausible (not that it would be plausible in most applications...). I'm guessing there is either some consistent bias in the data set that it making it overly easy for an SVM to pull out the answer, =or=, more likely, I've done something wrong on the SVM side.
I'm looking for suggestions to help understand what is going on--is it me (=my usage of LibSVM)? Or is it the data?
The details:
About ~2500 labeled data vectors/instances (transformed video frames of individuals--<20 individual persons total), binary classification problem. ~900 features/instance. Unbalanced data set at about a 1:4 ratio.
Ran subset.py to separate the data into test (500 instances) and train (remaining).
Ran "svm-train -t 0 ". (Note: apparently no need for '-w1 1 -w-1 4'...)
Ran svm-predict on the test file. Accuracy=100%!
Things tried:
Checked about 10 times over that I'm not training & testing on the same data files, through some inadvertent command-line argument error
re-ran subset.py (even with -s 1) multiple times and did train/test only multiple different data sets (in case I randomly upon the most magical train/test pa
ran a simple diff-like check to confirm that the test file is not a subset of the training data
svm-scale on the data has no effect on accuracy (accuracy=100%). (Although the number of support vectors does drop from nSV=127, bSV=64 to nBSV=72, bSV=0.)
((weird)) using the default RBF kernel (vice linear -- i.e., removing '-t 0') results in accuracy going to garbage(?!)
(sanity check) running svm-predict using a model trained on a scaled data set against an unscaled data set results in accuracy = 80% (i.e., it always guesses the dominant class). This is strictly a sanity check to make sure that somehow svm-predict is nominally acting right on my machine.
Tentative conclusion?:
Something with the data is wacked--somehow, within the data set, there is a subtle, experimenter-driven effect that the SVM is picking up on.
(This doesn't, on first pass, explain why the RBF kernel gives garbage results, however.)
Would greatly appreciate any suggestions on a) how to fix my usage of LibSVM (if that is actually the problem) or b) determine what subtle experimenter-bias in the data LibSVM is picking up on.
Two other ideas:
Make sure you're not training and testing on the same data. This sounds kind of dumb, but in computer vision applications you should take care that: make sure you're not repeating data (say two frames of the same video fall on different folds), you're not training and testing on the same individual, etc. It is more subtle than it sounds.
Make sure you search for gamma and C parameters for the RBF kernel. There are good theoretical (asymptotic) results that justify that a linear classifier is just a degenerate RBF classifier. So you should just look for a good (C, gamma) pair.
Notwithstanding that the devil is in the details, here are three simple tests you could try:
Quickie (~2 minutes): Run the data through a decision tree algorithm. This is available in Matlab via classregtree, or you can load into R and use rpart. This could tell you if one or just a few features happen to give a perfect separation.
Not-so-quickie (~10-60 minutes, depending on your infrastructure): Iteratively split the features (i.e. from 900 to 2 sets of 450), train, and test. If one of the subsets gives you perfect classification, split it again. It would take fewer than 10 such splits to find out where the problem variables are. If it happens to "break" with many variables remaining (or even in the first split), select a different random subset of features, shave off fewer variables at a time, etc. It can't possibly need all 900 to split the data.
Deeper analysis (minutes to several hours): try permutations of labels. If you can permute all of them and still get perfect separation, you have some problem in your train/test setup. If you select increasingly larger subsets to permute (or, if going in the other direction, to leave static), you can see where you begin to lose separability. Alternatively, consider decreasing your training set size and if you get separability even with a very small training set, then something is weird.
Method #1 is fast & should be insightful. There are some other methods I could recommend, but #1 and #2 are easy and it would be odd if they don't give any insights.
A user has to complete ten steps to achieve a desired result. The ten steps can be completed in any order.
If there is a bug, the bug is dependent only on the steps that have been taken, not the order in which they were taken (i.e., the bug is path independent).
For example: If the user performs three steps in the order 10, 1, 2 and produces a bug the exact same bug will be produced if the user performs the same three steps in the order 1, 2, 10.
What is the maximum number of unique bugs this program can have?
You mean what is the number of distinct sets pickable from 10 elements? That's a powerset: 2**10.
Much later: some knowledgeable others have suggested that having no bugs should not be counted as a bug. Accordingly, I revise my count: 2**10 - 1.
hughdbrown's answer is correct, but there is another possible interpretation of the question. Suppose that a sequence of operations can never produce more than one bug (i.e. that it should just be counted as one bug). For example, if the operations (3,6,2) is a bug, then you shouldn't be allowed to count (3,6,2,5) as another bug. In that case, rather than finding the maximum possible number of subsets of {1,2,...,10}, you want to find the maximum number of possible subsets so that no one contains another. The answer to this version of the question is "10 choose 5"=252.
Edit: by the way, the result that says this is maximal is called Sperner's Theorem.
It depends entirely how many ways there are of doing each step. If you have a process that involves only one step, but there are multiple ways of doing that step, every step could have an associated bug.
There's also the misuse of functions, which you cant prevent against, which could be considered a bug. ie:
If a user was to think that
rm -rf /
was short for
remove media --really fast /
ie: eject all devices1
I would guess that would be a potential bug. Its user error really, but its still a singular thing that can occur that produces results other than that were wanted.
You could argue the above is a bit over the top, but ultimately, there is no limitation on the ways users can do things wrong.
When users are there, assume, anything that can go wrong, will.
The only problem with the above reasoning, is you have to prematurely delete powerful things so users don't hurt themselves, which leads to less effective tools for those who know how to use them. Like corks on forks sort of rationale.
The only way to solve this concern effectively is give newbs blunt objects to learn with, and then give them an option which takes away all the foam padding once they learn the ropes, so experienced users don't have to keep working with blunt tools, and don't have to deblunten every tool themself.
( If there are infinite numbers of possibly ways to do 1 step, I don't even want to begin to think of the numbers of ways to do 10 steps wrong )
1: If you don't know, this will erase lots of your hard drive and cause much pain. Don't do it.
One, a designer fault? :)