Minor edits to benchmarking code (#1985)

* Make RandomActivationByType.agents_by_type backward compatible * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * updated warning * Update .gitignore * Update global_benchmark.py * pep8 rename * Update schelling.py * Update schelling.py * Update schelling.py * Update schelling.py * Squashed commit of the following: commit c0de4a1 Author: Ewout ter Hoeven <[email protected]> Date: Sun Jan 21 14:43:08 2024 +0100 Add CI workflow for performance benchmarks (#1983) This commit introduces a new GitHub Actions workflow for performance benchmarking. The workflow is triggered on `pull_request_target` events and can be triggered with a "/rerun-benchmarks" comment in the PR. It should be compatible with PRs from both forks and the main repository. It includes steps for setting up the environment, checking out the PR and main branches, installing dependencies, and running benchmarks on both branches. The results are then compared, encoded, and posted as a comment on the PR. * Revert "Squashed commit of the following:" This reverts commit 9485087. * Update .gitignore * further updates * Update benchmarks/WolfSheep/__init__.py --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
projectmesa · Jan 21, 2024 · 4d0eeec · 4d0eeec
1 parent 72b0a9d
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diff --git a/.gitignore b/.gitignore
@@ -1,5 +1,5 @@
 # Benchmarking
-benchmarking/**/*.pickle
+benchmarks/**/*.pickle
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -69,6 +69,7 @@ target/
 
 # PyCharm environment files
 .idea/
+*.pclprof
 
 # VSCode environment files
 .vscode/
@@ -88,3 +89,4 @@ pythonenv*/
 # JS dependencies
 mesa/visualization/templates/external/
 mesa/visualization/templates/js/external/
+
diff --git a/benchmarks/Flocking/Flocking.py b/benchmarks/Flocking/Flocking.py
diff --git a/benchmarks/Flocking/boid.py → benchmarks/Flocking/flocking.py b/benchmarks/Flocking/boid.py → benchmarks/Flocking/flocking.py
@@ -1,6 +1,15 @@
+"""
+Flockers
+=============================================================
+A Mesa implementation of Craig Reynolds's Boids flocker model.
+Uses numpy arrays to represent vectors.
+"""
+
 import numpy as np
 
-from mesa import Agent
+from mesa import Agent, Model
+from mesa.space import ContinuousSpace
+from mesa.time import RandomActivation
 
 
 class Boid(Agent):
@@ -79,3 +88,82 @@ def step(self):
         self.velocity /= np.linalg.norm(self.velocity)
         new_pos = self.pos + self.velocity * self.speed
         self.model.space.move_agent(self, new_pos)
+
+
+class BoidFlockers(Model):
+    """
+    Flocker model class. Handles agent creation, placement and scheduling.
+    """
+
+    def __init__(
+        self,
+        seed,
+        population,
+        width,
+        height,
+        vision,
+        speed=1,
+        separation=1,
+        cohere=0.03,
+        separate=0.015,
+        match=0.05,
+    ):
+        """
+        Create a new Flockers model.
+
+        Args:
+            population: Number of Boids
+            width, height: Size of the space.
+            speed: How fast should the Boids move.
+            vision: How far around should each Boid look for its neighbors
+            separation: What's the minimum distance each Boid will attempt to
+                    keep from any other
+            cohere, separate, match: factors for the relative importance of
+                    the three drives."""
+        super().__init__(seed=seed)
+        self.population = population
+        self.vision = vision
+        self.speed = speed
+        self.separation = separation
+        self.schedule = RandomActivation(self)
+        self.space = ContinuousSpace(width, height, True)
+        self.factors = {"cohere": cohere, "separate": separate, "match": match}
+        self.make_agents()
+
+    def make_agents(self):
+        """
+        Create self.population agents, with random positions and starting headings.
+        """
+        for i in range(self.population):
+            x = self.random.random() * self.space.x_max
+            y = self.random.random() * self.space.y_max
+            pos = np.array((x, y))
+            velocity = np.random.random(2) * 2 - 1
+            boid = Boid(
+                i,
+                self,
+                pos,
+                self.speed,
+                velocity,
+                self.vision,
+                self.separation,
+                **self.factors,
+            )
+            self.space.place_agent(boid, pos)
+            self.schedule.add(boid)
+
+    def step(self):
+        self.schedule.step()
+
+
+if __name__ == "__main__":
+    import time
+
+    # model = BoidFlockers(15, 200, 100, 100, 5)
+    model = BoidFlockers(15, 400, 100, 100, 15)
+
+    start_time = time.perf_counter()
+    for _ in range(100):
+        model.step()
+
+    print(time.perf_counter() - start_time)
diff --git a/benchmarks/Schelling/Schelling.py → benchmarks/Schelling/schelling.py b/benchmarks/Schelling/Schelling.py → benchmarks/Schelling/schelling.py
@@ -1,4 +1,3 @@
-import random
 
 from mesa import Agent, Model
 from mesa.space import SingleGrid
@@ -10,22 +9,20 @@ class SchellingAgent(Agent):
     Schelling segregation agent
     """
 
-    def __init__(self, pos, model, agent_type):
+    def __init__(self, unique_id, model, agent_type):
         """
         Create a new Schelling agent.
         Args:
            unique_id: Unique identifier for the agent.
            x, y: Agent initial location.
            agent_type: Indicator for the agent's type (minority=1, majority=0)
         """
-        super().__init__(pos, model)
-        self.pos = pos
+        super().__init__(unique_id, model)
         self.type = agent_type
 
     def step(self):
         similar = 0
-        r = self.model.radius
-        for neighbor in self.model.grid.iter_neighbors(self.pos, moore=True, radius=r):
+        for neighbor in self.model.grid.iter_neighbors(self.pos, moore=True, radius=self.model.radius):
             if neighbor.type == self.type:
                 similar += 1
 
@@ -36,7 +33,7 @@ def step(self):
             self.model.happy += 1
 
 
-class SchellingModel(Model):
+class Schelling(Model):
     """
     Model class for the Schelling segregation model.
     """
@@ -63,9 +60,9 @@ def __init__(
         # the coordinates of a cell as well as
         # its contents. (coord_iter)
         for _cont, pos in self.grid.coord_iter():
-            if random.random() < self.density:  # noqa: S311
-                agent_type = 1 if random.random() < self.minority_pc else 0  # noqa: S311
-                agent = SchellingAgent(pos, self, agent_type)
+            if self.random.random() < self.density:
+                agent_type = 1 if self.random.random() < self.minority_pc else 0
+                agent = SchellingAgent(self.next_id(), self, agent_type)
                 self.grid.place_agent(agent, pos)
                 self.schedule.add(agent)
 
@@ -75,3 +72,16 @@ def step(self):
         """
         self.happy = 0  # Reset counter of happy agents
         self.schedule.step()
+
+
+if __name__ == "__main__":
+    import time
+
+    # model = Schelling(15, 40, 40, 3, 1, 0.625)
+    model = Schelling(15, 100, 100, 8, 2, 0.8)
+
+    start_time = time.perf_counter()
+    for _ in range(100):
+        model.step()
+
+    print(time.perf_counter() - start_time)
diff --git a/benchmarks/WolfSheep/__init__.py b/benchmarks/WolfSheep/__init__.py
@@ -0,0 +1,14 @@
+from .wolf_sheep import WolfSheep
+
+if __name__ == "__main__":
+    # for profiling this benchmark model
+    import time
+
+    # model = WolfSheep(15, 25, 25, 60, 40, 0.2, 0.1, 20)
+    model = WolfSheep(15, 100, 100, 1000, 500, 0.4, 0.2, 20)
+
+    start_time = time.perf_counter()
+    for _ in range(100):
+        model.step()
+
+    print(time.perf_counter() - start_time)
diff --git a/benchmarks/WolfSheep/WolfSheep.py → benchmarks/WolfSheep/wolf_sheep.py b/benchmarks/WolfSheep/WolfSheep.py → benchmarks/WolfSheep/wolf_sheep.py
diff --git a/benchmarks/configurations.py b/benchmarks/configurations.py
@@ -1,10 +1,10 @@
-from Flocking.Flocking import BoidFlockers
-from Schelling.Schelling import SchellingModel
-from WolfSheep.WolfSheep import WolfSheep
+from Flocking.flocking import BoidFlockers
+from Schelling.schelling import Schelling
+from WolfSheep.wolf_sheep import WolfSheep
 
 configurations = {
     # Schelling Model Configurations
-    SchellingModel: {
+    Schelling: {
         "small": {
             "seeds": 50,
             "replications": 5,

diff --git a/benchmarks/global_benchmark.py b/benchmarks/global_benchmark.py
@@ -5,6 +5,10 @@
 import time
 import timeit
 
+# making sure we use this version of mesa and not one
+# also installed in site_packages or so.
+sys.path.insert(0, os.path.abspath(".."))
+
 from configurations import configurations
 
 
@@ -56,7 +60,7 @@ def run_experiments(model_class, config):
         mean_run = sum(results[1]) / len(results[1])
 
         print(
-            f"{time.strftime("%H:%M:%S", time.localtime())} {model.__name__:<14} ({size}) timings: Init {mean_init:.5f} s; Run {mean_run:.4f} s"
+            f"{time.strftime('%H:%M:%S', time.localtime())} {model.__name__:<14} ({size}) timings: Init {mean_init:.5f} s; Run {mean_run:.4f} s"
         )
 
         results_dict[model, size] = results