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More pythonic implementation of wolf sheep #2011

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Jan 29, 2024
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More pythonic implementation of wolf sheep #2011

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more pythonic implementation of wolf sheep
quaquel Jan 27, 2024
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[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jan 27, 2024
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51 changes: 32 additions & 19 deletions benchmarks/WolfSheep/wolf_sheep.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,15 +8,13 @@
Center for Connected Learning and Computer-Based Modeling,
Northwestern University, Evanston, IL.
"""
import math

from mesa import Model, Agent
from mesa import Agent, Model
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These 3 lines can be condensed to be just import mesa, then calling them would be mesa.Agent, mesa.Model, mesa.space.MultiGrid, in line with np.array or pd.DataFrame. See https://github.com/projectmesa/mesa-examples/blob/479eaf389e1a98bda0bdba5aa8f24ede0d84e764/examples/wolf_sheep/wolf_sheep/model.py#L18.

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@quaquel quaquel Jan 27, 2024 •
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But that is slower.

To be clear, I don't care much either way, so if there is some mesa convention for this, I am fine changing it.

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It's about the same:

In [1]: import mesa

In [2]: m = mesa.Model()

In [3]: %timeit mesa.Agent(0, m)
627 ns ± 125 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

In [4]: from mesa import Agent, Model

In [5]: m2 = Model()

In [6]: %timeit Agent(0, m2)
627 ns ± 148 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

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Except for the import lines, this PR LGTM.

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I really don't have strong opinions on this so if others want to change this as well, fine.

Note, however, that the Schelling model already uses the same style of importing as I have used here. I also personally prefer e.g., SchellingAgent(Agent) over SchellingAgent(mesa.Agent). This is really just a matter of taste.

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The convention in the examples repo all uses mesa.x. The ones in the Agents.jl benchmark code are from the outdated version of the examples.

This is really just a matter of taste.

But in the case of Agents.jl benchmark, they count and compare the LOC.

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The convention in the examples repo all uses mesa.x.

I ment our benchmarks

But in the case of Agents.jl benchmark, they count and compare the LOC.

And we agree that this is only a rough proxy we should not obsess about.

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I ment our benchmarks

The 3 versions of the code (mesa-examples, core Mesa benchmark, Agents.jl) ideally need to be kept in sync.

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I agree. Once this is merged, I'll happily make PRs on the other libraries.

from mesa.space import MultiGrid
from mesa.time import RandomActivationByType


class Animal(Agent):

def __init__(self, unique_id, model, moore, energy, p_reproduce, energy_from_food):
super().__init__(unique_id, model)
self.energy = energy
Expand All @@ -33,7 +31,12 @@ def random_move(self):
def spawn_offspring(self):
self.energy /= 2
offspring = self.__class__(
self.model.next_id(), self.model, self.moore, self.energy, self.p_reproduce, self.energy_from_food
self.model.next_id(),
self.model,
self.moore,
self.energy,
self.p_reproduce,
self.energy_from_food,
)
self.model.grid.place_agent(offspring, self.pos)
self.model.schedule.add(offspring)
Expand Down Expand Up @@ -72,6 +75,7 @@ def feed(self):
self.energy += self.energy_from_food
grass_patch.fully_grown = False


class Wolf(Animal):
"""
A wolf that walks around, reproduces (asexually) and eats sheep.
Expand Down Expand Up @@ -123,18 +127,18 @@ class WolfSheep(Model):
"""

def __init__(
self,
seed,
height,
width,
initial_sheep,
initial_wolves,
sheep_reproduce,
wolf_reproduce,
grass_regrowth_time,
wolf_gain_from_food=13,
sheep_gain_from_food=5,
moore=False
self,
seed,
height,
width,
initial_sheep,
initial_wolves,
sheep_reproduce,
wolf_reproduce,
grass_regrowth_time,
wolf_gain_from_food=13,
sheep_gain_from_food=5,
moore=False,
):
"""
Create a new Wolf-Sheep model with the given parameters.
Expand Down Expand Up @@ -168,7 +172,14 @@ def __init__(
self.random.randrange(self.height),
)
energy = self.random.randrange(2 * sheep_gain_from_food)
sheep = Sheep(self.next_id(), self, moore, energy, sheep_reproduce, sheep_gain_from_food)
sheep = Sheep(
self.next_id(),
self,
moore,
energy,
sheep_reproduce,
sheep_gain_from_food,
)
self.grid.place_agent(sheep, pos)
self.schedule.add(sheep)

Expand All @@ -179,7 +190,9 @@ def __init__(
self.random.randrange(self.height),
)
energy = self.random.randrange(2 * wolf_gain_from_food)
wolf = Wolf(self.next_id(), self, moore, energy, wolf_reproduce, wolf_gain_from_food)
wolf = Wolf(
self.next_id(), self, moore, energy, wolf_reproduce, wolf_gain_from_food
)
self.grid.place_agent(wolf, pos)
self.schedule.add(wolf)

Expand Down Expand Up @@ -207,4 +220,4 @@ def step(self):
start_time = time.perf_counter()
for _ in range(100):
model.step()
print("Time:", time.perf_counter() - start_time)
print("Time:", time.perf_counter() - start_time)
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