New functions: random_id_in_position & random_agent_in_position #842

Tortar · 2023-08-08T00:21:44Z

These are the functions we need for WolfSheep in the benchmark repo. And they are also useful in general.

There is also another important change in this PR -> a new keyword alloc for all the filtered random searches: this makes it possible to choose between the best method for each case:

allocation + evaluation of the filtering on just some members
no allocation + evaluation of the filtering on all members

Notice that alloc is currently set to False (even if some benchmarks are needed to say if it is better to set it so), which means that the new methodology is currently being tested

This is still a rough PR, many things are a bit redundant or can be improved, docstrings are lacking, etc...

codecov-commenter · 2023-08-08T00:32:06Z

Codecov Report

Merging #842 (2ee4687) into main (3ac17b8) will increase coverage by 0.56%.
The diff coverage is 98.21%.

@@            Coverage Diff             @@
##             main     #842      +/-   ##
==========================================
+ Coverage   70.18%   70.75%   +0.56%     
==========================================
  Files          42       42              
  Lines        2727     2773      +46     
==========================================
+ Hits         1914     1962      +48     
+ Misses        813      811       -2

Files Changed	Coverage Δ
src/spaces/graph.jl	`76.19% <ø> (+0.80%)`	⬆️
src/core/model_abstract.jl	`90.76% <91.66%> (+4.76%)`	⬆️
src/core/space_interaction_API.jl	`92.64% <100.00%> (+0.64%)`	⬆️
src/spaces/discrete.jl	`97.61% <100.00%> (+0.84%)`	⬆️

📣 We’re building smart automated test selection to slash your CI/CD build times. Learn more

Tortar · 2023-08-08T16:42:01Z

@Datseris if you want to take a look, this is ready for review, this can be used to close JuliaDynamics/ABMFrameworksComparison#66

We should still run some benchmarks to see how this new version with alloc performs

Tortar · 2023-08-08T19:31:30Z

yes, this way is better (alloc = false) than the previous one as a default:

julia> using Agents, Random, BenchmarkTools

julia> mutable struct LabelledAgent <: AbstractAgent
           id::Int
           label::Bool
       end

julia> function create_model(ModelType, n_agents_with_condition, n_agents=10000)
           agents = [LabelledAgent(id, id<=n_agents_with_condition) for id in 1:n_agents]
           model = ModelType(LabelledAgent)
           for a in agents
               add_agent!(a, model)
           end
           return model
       end
create_model (generic function with 2 methods)

julia> cond(agent) = agent.label
cond (generic function with 1 method)

julia> # common condition

       # UnremovableABM
       @benchmark random_agent(model, $cond, optimistic=false, alloc=false) setup=(model=create_model(UnremovableABM, 2000))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):   8.455 μs … 82.620 μs  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     10.311 μs              ┊ GC (median):    0.00%
 Time  (mean ± σ):   10.461 μs ±  1.621 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%

                             ▅█▅▅▂
  ▂▁▁▂▂▂▂▂▁▂▂▁▂▂▂▂▂▂▂▂▂▂▂▂▃▆████████▇▇▆▅▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▃
  8.46 μs         Histogram: frequency by time        12.1 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark random_agent(model, $cond, optimistic=false, alloc=true) setup=(model=create_model(UnremovableABM, 2000))
BenchmarkTools.Trial: 10000 samples with 7 evaluations.
 Range (min … max):   4.624 μs … 235.504 μs  ┊ GC (min … max):  0.00% … 94.15%
 Time  (median):      6.080 μs               ┊ GC (median):     0.00%
 Time  (mean ± σ):   10.228 μs ±  24.123 μs  ┊ GC (mean ± σ):  38.44% ± 15.34%

  █▃                                                           ▁
  ███▆▆▇▄▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▅▇█▇▇▇▆ █
  4.62 μs       Histogram: log(frequency) by time       150 μs <

 Memory estimate: 78.17 KiB, allocs estimate: 2.

julia> # DictionaryABM
       @benchmark random_agent(model, $cond, optimistic=false, alloc=false) setup=(model=create_model(StandardABM, 2000))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  49.614 μs … 445.393 μs  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     52.001 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   53.021 μs ±   5.342 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%

   ▁▃▅▆▇███▆▅▅▅▆▅▄▄▃▂▂▁▁▁▁▁▁▁▁                                 ▂
  ▆██████████████████████████████████▇███▇▇▆▅▆▆▄▆▄▅▆▆▅▄▂▄▅▅▅▅▆ █
  49.6 μs       Histogram: log(frequency) by time      66.8 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark random_agent(model, $cond, optimistic=false, alloc=true) setup=(model=create_model(StandardABM, 2000))

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  41.191 μs …  1.718 ms  ┊ GC (min … max):  0.00% … 95.23%
 Time  (median):     43.470 μs              ┊ GC (median):     0.00%
 Time  (mean ± σ):   51.051 μs ± 96.665 μs  ┊ GC (mean ± σ):  12.29% ±  6.28%

  ▂▅▆█▇▆▆▆▄▂▂▂▂▁▁▁▁▁▁                                         ▂
  █████████████████████▇▇▇▆██▇▆▇▆▇▆▆▇▇▇▇▅▇▅▆▆▅▄▃▆▃▅▄▃▅▃▄▃▄▄▁▃ █
  41.2 μs      Histogram: log(frequency) by time      71.6 μs <

 Memory estimate: 78.17 KiB, allocs estimate: 2.

julia> # rare condition

       # UnremovableABM
       @benchmark random_agent(model, $cond, optimistic=false, alloc=false) setup=(model=create_model(UnremovableABM, 2))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):   8.217 μs … 114.933 μs  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     10.311 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   10.493 μs ±   2.668 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%

                         ▂▇▇█▅▄▁
  ▂▁▁▂▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▄▇█████████▇▅▄▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂ ▃
  8.22 μs         Histogram: frequency by time           13 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark random_agent(model, $cond, optimistic=false, alloc=true) setup=(model=create_model(UnremovableABM, 2))

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):   4.906 μs …   1.574 ms  ┊ GC (min … max):  0.00% … 91.35%
 Time  (median):     29.230 μs               ┊ GC (median):     0.00%
 Time  (mean ± σ):   40.121 μs ± 107.777 μs  ┊ GC (mean ± σ):  19.43% ±  7.14%

   ▄▆██▇▇▅▅▇▅▄▅▄▅▄▂▄▄▃▂▂▃▂▂▁▂▁▁▁
  ▄██████████████████████████████▇█▇▇▆▆▆▆▅▆▅▄▄▄▃▃▃▂▂▂▂▁▁▁▁▁▁▁▁ ▅
  4.91 μs         Histogram: frequency by time         89.1 μs <

 Memory estimate: 78.17 KiB, allocs estimate: 2.

julia> # DictionaryABM
       @benchmark random_agent(model, $cond, optimistic=false, alloc=false) setup=(model=create_model(StandardABM, 2))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  49.570 μs … 181.012 μs  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     52.338 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   53.540 μs ±   4.754 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%

    ▃▄▆▇██▇▅▅▅▅▅▄▃▃▂▂▁▂▂▂▁▂▁▁▁▁▁  ▁                            ▃
  ▅██████████████████████████████▇███▇▇▆▆▆▅▅▅▅▆▆▆▆▄▆▆▅▅▆▄▅▁▅▅▅ █
  49.6 μs       Histogram: log(frequency) by time      71.1 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark random_agent(model, $cond, optimistic=false, alloc=true) setup=(model=create_model(StandardABM, 2))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):   42.938 μs …   2.115 ms  ┊ GC (min … max): 0.00% … 86.96%
 Time  (median):     101.398 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   115.503 μs ± 109.425 μs  ┊ GC (mean ± σ):  5.50% ±  5.95%

  ▄▇▇▇▇█▆▇▇▇█▇▆▆▆▅▄▅▅▄▄▄▃▃▄▃▃▂▂▁▁ ▁ ▁▁
  ████████████████████████████████████▇▆██▇▆▆▇▆▆▅▄▄▅▃▄▃▃▃▂▂▂▂▁▁ ▆
  42.9 μs          Histogram: frequency by time          231 μs <

 Memory estimate: 78.17 KiB, allocs estimate: 2.

for a common (simple) condition the mean time is almost equal to the previous one, for a rare (simple) condition is 4x in unremovableabm and 2x in standardabm in this setup

Tortar · 2023-08-08T23:12:00Z

the eat! method in WolfSheep now it's simpler and faster:

function eat!(wolf::Wolf, model)
    is_sheep(agent) = typeof(agent) == Sheep
    dinner = random_agent_in_position(wolf.pos, model, is_sheep)
    if !isnothing(dinner)
        remove_agent!(dinner, model)
        wolf.energy += wolf.Δenergy
    end
end

the wolfsheep model passed from 96 ms to 84 ms!

Datseris · 2023-08-10T09:17:45Z

i don't understand why these fnctions exist when one can use r=0 in the existing functions?

Tortar · 2023-08-10T11:51:57Z

I would say for mainly three reasons: we have ids_in_position and agents_in_position already (while you can have them with r = 0), writing r = 0 is strange (to me) and undocumented, using r = 0 without any change will have performance drawbacks since it creates an iterator while it is unnecessary and if we create a fastpath for r = 0 the function will become type-unstable in the returning value, which can possibly have some (albeit small) perf problems.
While most of these problems are solvable with a different implementation of the change, I think It is a bit better to add these two functions.

Datseris

ok

Datseris · 2023-08-10T12:06:45Z

You should cross-refernce these funtions in ids_in_position and random_nearby... Then merge!

new functionalities

de9dfad

Tortar changed the title ~~New function: random_id_in_position & random_agent_in_position~~ New functions: random_id_in_position & random_agent_in_position Aug 8, 2023

Tortar and others added 11 commits August 8, 2023 13:24

Update discrete.jl

4563faa

Update discrete.jl

6c384c6

Update discrete.jl

ba9f31b

Update discrete.jl

f0ef874

Update discrete.jl

4fff1c3

better logic

9a27339

tests

91fd531

tests 2

725bd20

docstrings

d4ea713

s

6818eae

ss

19df21f

changelof

a915a5c

Tortar added 2 commits August 9, 2023 00:33

Update space_interaction_API.jl

5c6f7d3

fix + faster

6d0fbff

Datseris approved these changes Aug 10, 2023

View reviewed changes

Tortar added 3 commits August 10, 2023 15:48

cross-ref

e5938f7

cross-ref2

7bc3c37

cross-ref3

2ee4687

Tortar merged commit d94ae1e into main Aug 10, 2023
5 checks passed

Tortar deleted the new-random-functionalities branch August 10, 2023 15:55

Tortar mentioned this pull request Aug 19, 2023

Updates for Agents.jl 6.0 JuliaDynamics/ABMFrameworksComparison#67

Merged

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

New functions: random_id_in_position & random_agent_in_position #842

New functions: random_id_in_position & random_agent_in_position #842

Tortar commented Aug 8, 2023 •

edited

Loading

codecov-commenter commented Aug 8, 2023 •

edited

Loading

Tortar commented Aug 8, 2023 •

edited

Loading

Tortar commented Aug 8, 2023 •

edited

Loading

Tortar commented Aug 8, 2023

Datseris commented Aug 10, 2023

Tortar commented Aug 10, 2023 •

edited

Loading

Datseris left a comment

Datseris commented Aug 10, 2023

New functions: random_id_in_position & random_agent_in_position #842

New functions: random_id_in_position & random_agent_in_position #842

Conversation

Tortar commented Aug 8, 2023 • edited Loading

codecov-commenter commented Aug 8, 2023 • edited Loading

Codecov Report

Tortar commented Aug 8, 2023 • edited Loading

Tortar commented Aug 8, 2023 • edited Loading

Tortar commented Aug 8, 2023

Datseris commented Aug 10, 2023

Tortar commented Aug 10, 2023 • edited Loading

Datseris left a comment

Choose a reason for hiding this comment

Datseris commented Aug 10, 2023

Tortar commented Aug 8, 2023 •

edited

Loading

codecov-commenter commented Aug 8, 2023 •

edited

Loading

Tortar commented Aug 8, 2023 •

edited

Loading

Tortar commented Aug 8, 2023 •

edited

Loading

Tortar commented Aug 10, 2023 •

edited

Loading