idmtools.builders.arm_simulation_builder module¶
idmtools arm builder definition.
Copyright 2021, Bill & Melinda Gates Foundation. All rights reserved.
- class idmtools.builders.arm_simulation_builder.ArmType(value)¶
Bases:
enum.Enum
ArmTypes.
- cross = 0¶
- pair = 1¶
- class idmtools.builders.arm_simulation_builder.SweepArm(type=ArmType.cross, funcs: Optional[List[Tuple[Callable, Iterable]]] = None)¶
Bases:
object
Class that represents a parameter arm.
- __init__(type=ArmType.cross, funcs: Optional[List[Tuple[Callable, Iterable]]] = None)¶
Constructor.
- Parameters:
type – Type of Arm(Cross or Pair)
funcs – Functions to add as sweeps
- add_sweep_definition(func: Callable, values: Iterable[Any])¶
Add Sweep definition.
- Parameters:
func – Sweep callback
values – Values to Sweep
- Returns:
None
- get_max_values_count()¶
Get the max values count from different sweep functions.
- Returns:
Max values
- adjust_values_length()¶
Adjust values length.
- Returns:
None
- class idmtools.builders.arm_simulation_builder.ArmSimulationBuilder¶
Bases:
idmtools.builders.simulation_builder.SimulationBuilder
Class that represents an experiment builder.
This particular sweep builder build sweeps in “ARMS”. This is particular useful in situations where you want to sweep parameters that have branches of parameters. For Example, let’s say we have a model with the following parameters: * population * susceptible * recovered * enable_births * birth_rate
Enable births controls an optional feature that is controlled by the birth_rate parameter. If we want to sweep a set of parameters on population, susceptible with enabled_births set to off but also want to sweep the birth_rate we could do that like so
################################### # This example provides how you can check if your sweeps are working as expected ################################### from functools import partial from idmtools.builders import ArmSimulationBuilder, SweepArm, ArmType from idmtools.entities.command_task import CommandTask from idmtools.entities.templated_simulation import TemplatedSimulations from tabulate import tabulate def update_parameter(simulation, parameter, value): simulation.task.config[parameter] = value base_task = CommandTask('example') base_task.config = dict(enable_births=False) builder = ArmSimulationBuilder() # Define our first set of sweeps arm = SweepArm(type=ArmType.cross) arm.add_sweep_definition(partial(update_parameter, parameter='population'), [500, 1000]) arm.add_sweep_definition(partial(update_parameter, parameter='susceptible'), [0.5, 0.9]) builder.add_arm(arm) # Now add the sweeps with the birth_rate as well arm.add_sweep_definition(partial(update_parameter, parameter='enable_births'), [True]) arm.add_sweep_definition(partial(update_parameter, parameter='birth_rate'), [0.01, 0.1]) builder.add_arm(arm) sims = TemplatedSimulations(base_task=base_task) sims.add_builder(builder) print(tabulate([s.task.config for s in list(sims)], headers="keys"))
This would result in the output
¶ enable_births
population
susceptible
birth_rate
False
500
0.5
False
500
0.9
False
1000
0.5
False
1000
0.9
True
500
0.5
0.01
True
500
0.5
0.1
True
500
0.9
0.01
True
500
0.9
0.1
True
1000
0.5
0.01
True
1000
0.5
0.1
True
1000
0.9
0.01
True
1000
0.9
0.1
Examples
""" This file demonstrates how to use ArmExperimentBuilder in PythonExperiment's builder. We are then adding the builder to PythonExperiment. |__sweep arm1 |_ a = 1 |_ b = [2,3] |_ c = [4,5] |__ sweep arm2 |_ a = [6,7] |_ b = 2 Expect sims with parameters: sim1: {a:1, b:2, c:4} sim2: {a:1, b:2, c:5} sim3: {a:1, b:3, c:4} sim4: {a:1, b:3, c:5} sim5: {a:6, b:2} sim6: {a:7, b:2} Note: arm1 and arm2 are adding to total simulations """ import os import sys from functools import partial from idmtools.builders import SweepArm, ArmType, ArmSimulationBuilder from idmtools.core.platform_factory import platform from idmtools.entities.experiment import Experiment from idmtools.entities.templated_simulation import TemplatedSimulations from idmtools_models.python.json_python_task import JSONConfiguredPythonTask from idmtools_test import COMMON_INPUT_PATH # define specific callbacks for a, b, and c setA = partial(JSONConfiguredPythonTask.set_parameter_sweep_callback, param="a") setB = partial(JSONConfiguredPythonTask.set_parameter_sweep_callback, param="b") setC = partial(JSONConfiguredPythonTask.set_parameter_sweep_callback, param="c") if __name__ == "__main__": with platform('BELEGOST'): base_task = JSONConfiguredPythonTask(script_path=os.path.join(COMMON_INPUT_PATH, "python", "model1.py")) # define that we are going to create multiple simulations from this task ts = TemplatedSimulations(base_task=base_task) # define our first sweep Sweep Arm arm1 = SweepArm(type=ArmType.cross) builder = ArmSimulationBuilder() arm1.add_sweep_definition(setA, 1) arm1.add_sweep_definition(setB, [2, 3]) arm1.add_sweep_definition(setC, [4, 5]) builder.add_arm(arm1) # adding more simulations with sweeping arm2 = SweepArm(type=ArmType.cross) arm2.add_sweep_definition(setA, [6, 7]) arm2.add_sweep_definition(setB, [2]) builder.add_arm(arm2) # add our builders to our template ts.add_builder(builder) # create experiment from the template experiment = Experiment.from_template(ts, name=os.path.split(sys.argv[0])[1], tags={"string_tag": "test", "number_tag": 123, "KeyOnly": None}) # run the experiment experiment.run() # in most real scenarios, you probably do not want to wait as this will wait until all simulations # associated with an experiment are done. We do it in our examples to show feature and to enable # testing of the scripts experiment.wait() # use system status as the exit code sys.exit(0 if experiment.succeeded else -1)
- __init__()¶
Constructor.
- add_arm(arm)¶
Add arm sweep definition.
- Parameters:
arm – Arm to add
- Returns:
None