Update example scripts

scripts/stats_compare_basic_agents.py

@@ -2,8 +2,9 @@
 Run all the basic agents with a number of actions per turn using a MultiSim (n reps = 100). Doesn't save .gifs of
 each rep, rather plots distributions of final scores.
 
-Parameters here match the visual version run in scripts/stats_compare_basic_agents.py.
+Parameters here are similar to the visual version run in scripts/visual_compare_basic_agents.py.
 """
+from typing import List
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -15,7 +16,8 @@
 import social_distancing_sim.sim as sim
 
 
-def plot_dists(result: str = "Overall score") -> plt.Figure:
+def plot_dists(multi_sims: List[sim.MultiSim],
+ result: str = "Overall score") -> plt.Figure:
  """Plot final score distributions across repetitions, for all agents."""
  fig, axs = plt.subplots(nrows=4,
  ncols=1,
@@ -84,17 +86,17 @@ def plot_dists(result: str = "Overall score") -> plt.Figure:
  n_steps=125)
 
  multi_sims.append(sim.MultiSim(sim_,
- name='basic agent comparison 2',
+ name='basic agent comparison',
  n_reps=100))
 
  # Run all the sims. No need to parallelize here as it's done across n reps in MultiSim.run()
  for ms in tqdm(multi_sims):
  ms.run()
 
- fig = plot_dists("Overall score")
+ fig = plot_dists(multi_sims, "Overall score")
  plt.show()
- fig.savefig('agent_comparison_score.png')
+ fig.savefig('basic_agent_comparison_score.png')
 
- fig = plot_dists("Total deaths")
+ fig = plot_dists(multi_sims, "Total deaths")
  plt.show()
- fig.savefig('agent_comparison_deaths.png')
+ fig.savefig('basic_agent_comparison_deaths.png')

scripts/stats_compare_multi_agents.py

@@ -0,0 +1,131 @@
+"""
+Run all the basic agents with a number of actions per turn using a MultiSim (n reps = 100). Doesn't save .gifs of
+each rep, rather plots distributions of final scores.
+
+Parameters here are similar to the visual version run in scripts/visual_compare_multi_agents.py.
+"""
+from typing import List
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tqdm import tqdm
+
+import social_distancing_sim.agent as agent
+import social_distancing_sim.environment as env
+import social_distancing_sim.sim as sim
+
+
+def plot_dists(multi_sims: List[sim.MultiSim],
+ result: str = "Overall score") -> plt.Figure:
+ """Plot final score distributions across repetitions, for all agents."""
+ fig, ax = plt.subplots(nrows=1,
+ ncols=1,
+ figsize=(8, 8))
+
+ min_score = 0
+ max_score = 0
+ for run in multi_sims:
+ min_score = min(min_score, run.results[result].min())
+ max_score = max(max_score, run.results[result].max())
+
+ sns.distplot(run.results[result],
+ hist=False,
+ label=run.sim.agent.name)
+
+ ax.set_title("Policy comparison",
+ fontweight='bold')
+ ax.set_xlim([min_score - abs(min_score * 0.2), max_score + abs(max_score * 0.2)])
+ ax.set_xlabel(ax.get_xlabel(),
+ fontweight='bold')
+ ax.set_ylabel('Prop.',
+ fontweight='bold')
+ ax.legend(title='Agent')
+
+ return fig
+
+
+if __name__ == "__main__":
+ seed = 123
+ steps = 250
+ distancing_params = {"actions_per_turn": 15,
+ "start_step": {'isolate': 15, 'reconnect': 60},
+ "end_step": {'isolate': 55, 'reconnect': steps}}
+ vaccination_params = {"actions_per_turn": 5,
+ "start_step": {'vaccinate': 60},
+ "end_step": {'vaccinate': steps}}
+ treatment_params = {"actions_per_turn": 5,
+ "start_step": {'treat': 50},
+ "end_step": {'treat': steps}}
+
+ # Create a parameter set containing all combinations of the 3 policy agents, and a small set of n_actions
+ agents = [agent.MultiAgent(name="Distancing",
+ agents=[agent.DistancingPolicyAgent(**distancing_params)]),
+ agent.MultiAgent(name="Vaccination",
+ agents=[agent.VaccinationPolicyAgent(**vaccination_params)]),
+ agent.MultiAgent(name="Treatment",
+ agents=[agent.TreatmentPolicyAgent(**treatment_params)]),
+ agent.MultiAgent(name="Distancing, vaccination",
+ agents=[agent.DistancingPolicyAgent(**distancing_params),
+ agent.VaccinationPolicyAgent(**vaccination_params)]),
+ agent.MultiAgent(name="Distancing, treatment",
+ agents=[agent.DistancingPolicyAgent(**distancing_params),
+ agent.TreatmentPolicyAgent(**treatment_params)]),
+ agent.MultiAgent(name="Vaccination, treatment",
+ agents=[agent.VaccinationPolicyAgent(**vaccination_params),
+ agent.TreatmentPolicyAgent(**treatment_params)]),
+ agent.MultiAgent(name="Distancing, vaccination, treatment",
+ agents=[agent.DistancingPolicyAgent(**distancing_params),
+ agent.VaccinationPolicyAgent(**vaccination_params),
+ agent.TreatmentPolicyAgent(**treatment_params)])]
+
+ # Loop over the parameter set and create the Agents, Environments, and the Sim handler
+ multi_sims = []
+ for agt in agents:
+ # Name the environment according to the agent used
+ env_ = env.Environment(name=f"{type(agt).__name__} - {agt.name}",
+ action_space=env.ActionSpace(vaccinate_cost=0,
+ treat_cost=0,
+ isolate_cost=0,
+ isolate_efficiency=0.70,
+ reconnect_efficiency=0.2,
+ treatment_conclusion_chance=0.5,
+ treatment_recovery_rate_modifier=1.8,
+ vaccinate_efficiency=1.25),
+ disease=env.Disease(name='COVID-19',
+ virulence=0.0055,
+ seed=None,
+ immunity_mean=0.7,
+ recovery_rate=0.9,
+ immunity_decay_mean=0.01),
+ healthcare=env.Healthcare(capacity=200),
+ observation_space=env.ObservationSpace(
+ graph=env.Graph(community_n=30,
+ community_size_mean=20,
+ community_p_out=0.08,
+ community_p_in=0.16,
+ seed=None),
+ test_rate=1,
+ seed=None),
+ initial_infections=5,
+ random_infection_chance=1,
+ seed=None)
+
+ sim_ = sim.Sim(env=env_,
+ agent=agt,
+ n_steps=150)
+
+ multi_sims.append(sim.MultiSim(sim_,
+ name='policy agent comparison',
+ n_reps=100))
+
+ # Run all the sims. No need to parallelize here as it's done across n reps in MultiSim.run()
+ for ms in tqdm(multi_sims):
+ ms.run()
+
+ fig = plot_dists(multi_sims, "Overall score")
+ plt.show()
+ fig.savefig('multi_agent_comparison_score.png')
+
+ fig = plot_dists(multi_sims, "Total deaths")
+ plt.show()
+ fig.savefig('multi_agent_comparison_deaths.png')

scripts/visual_compare_policy_agents.py

@@ -8,7 +8,6 @@
 import social_distancing_sim.agent as agent
 import social_distancing_sim.environment as env
 import social_distancing_sim.sim as sim
-from social_distancing_sim.environment.action_space import ActionSpace
 
 
 def run_and_replay(sim):
@@ -44,16 +43,17 @@ def run_and_replay(sim):
 
  # Name the environment according to the agent used
  env_ = env.Environment(name=f"{type(agt_).__name__} - {n_act} actions",
- action_space=ActionSpace(isolate_efficiency=0.5,
- vaccinate_efficiency=0.95),
+ action_space=env.ActionSpace(isolate_efficiency=0.5,
+  vaccinate_efficiency=0.95),
  disease=env.Disease(name='COVID-19',
  virulence=0.008,
  seed=seed,
  immunity_mean=0.7,
  recovery_rate=0.9,
  immunity_decay_mean=0.01),
  healthcare=env.Healthcare(capacity=75),
- environment_plotting=env.EnvironmentPlotting(ts_fields_g2=["Actions taken"]),
+ environment_plotting=env.EnvironmentPlotting(ts_fields_g2=["Actions taken",
+ "Overall score"]),
  observation_space=env.ObservationSpace(
  graph=env.Graph(community_n=20,
  community_size_mean=15,
@@ -71,6 +71,6 @@ def run_and_replay(sim):
  save=True,
  tqdm_on=True)) # Show progress bars for running sims
 
-  # Run all the prepared Sims
+ # Run all the prepared Sims
  Parallel(n_jobs=4,
  backend='loky')(delayed(run_and_replay)(sim) for sim in sims)

scripts/visual_compare_testing_rate.py

@@ -5,7 +5,7 @@
 
  pop = env.Environment(name='A herd of cats, observed',
  disease=env.Disease(name='COVID-19'),
- healthcare=env.healthcare(),
+ healthcare=env.Healthcare(),
  observation_space=env.ObservationSpace(graph=env.Graph(community_n=40,
  community_size_mean=16,
  seed=123),

scripts/visual_run_simulation_with_agent.py

@@ -24,8 +24,8 @@
  "Observed overall score"]))
 
  sim = sim.Sim(env=pop,
- agent=env.accinationAgent(actions_per_turn=25,
- seed=seed),
+ agent=env.VaccinationAgent(actions_per_turn=25,
+  seed=seed),
  plot=True,
  save=True)