def test_batch_run_with_params():
batch_run(
MockModel,
{
"variable_model_params": range(5),
"variable_agent_params": ["H", "E", "L", "L", "O"],
},
)
def test_batch_run():
result = batch_run(MockModel, {})
assert result == [
{
"RunId": 0,
"iteration": 0,
"Step": 1000,
"reported_model_param": 42,
"AgentID": 0,
"agent_id": 0,
"agent_local": 250.0,
},
{
"RunId": 0,
"iteration": 0,
"Step": 1000,
"reported_model_param": 42,
"AgentID": 1,
"agent_id": 1,
"agent_local": 250.0,
},
{
"RunId": 0,
"iteration": 0,
"Step": 1000,
"reported_model_param": 42,
"AgentID": 2,
"agent_id": 2,
"agent_local": 250.0,
},
]
def test_batch_run_no_agent_reporters():
result = batch_run(MockModel, {"enable_agent_reporters": False})
print(result)
assert result == [{
"RunId": 0,
"iteration": 0,
"Step": 1000,
"enable_agent_reporters": False,
"reported_model_param": 42,
}]
def run_model(self):
for i in range(self.run_time):
self.step()
# parameter lists for each parameter to be tested in batch run
br_params = {
"init_people": [25, 100],
"rich_threshold": [5, 10],
"reserve_percent": 5,
}
if __name__ == "__main__":
data = batch_run(
BankReservesModel,
br_params,
model_reporters={"Rich": get_num_rich_agents},
agent_reporters={"Wealth": "wealth"},
)
br_df = pd.DataFrame(data)
br_df.to_csv("BankReservesModel_Data.csv")
# The commented out code below is the equivalent code as above, but done
# via the legacy BatchRunner class. This is a good example to look at if
# you want to migrate your code to use `batch_run()` from `BatchRunner`.
"""
br = BatchRunner(
BankReservesModel,
br_params,
iterations=2,
max_steps=1000,
nr_processes=None,
def test_batch_run_single_core():
batch_run(MockModel, {}, nr_processes=1, iterations=10)