def start_sequential_strategy(wf):
""" executes all experiments from the definition file """
info("> ExecStrategy | Sequential", Fore.CYAN)
wf.totalExperiments = len(wf.execution_strategy["knobs"])
for kn in wf.execution_strategy["knobs"]:
experimentFunction(wf, {
"knobs":kn,
"ignore_first_n_results": wf.execution_strategy["ignore_first_n_results"],
"sample_size": wf.execution_strategy["sample_size"],
})
def start_forever_strategy(wf):
""" executes forever - changes must come from definition file """
info("> ExecStrategy | Forever ", Fore.CYAN)
wf.totalExperiments = -1
while True:
experimentFunction(
wf, {
"knobs": {
"forever": True
},
"ignore_first_n_results":
wf.execution_strategy["ignore_first_n_results"],
"sample_size":
wf.execution_strategy["sample_size"],
})
def evolutionary_execution(wf, opti_values, variables):
global crowdnav_instance_number
""" this is the function we call and that returns a value for optimization """
knob_object = recreate_knob_from_optimizer_values(variables, opti_values)
# create a new experiment to run in execution
exp = dict()
# TODO where do we start multiple threads to call the experimentFunction concurrently, once for each experiment and crowdnav instance?
# TODO should we create new/fresh CrowdNav instances for each iteration/generation? Otherwise, we use the same instance to evaluate across interations/generations to evaluate individiuals.
if wf.execution_strategy["parallel_execution_of_individuals"]:
wf.primary_data_provider[
"instance"].topic = original_primary_data_provider_topic + "-" + str(
crowdnav_instance_number)
wf.change_provider[
"instance"].topic = original_change_provider_topic + "-" + str(
crowdnav_instance_number)
info("Listering on " + wf.primary_data_provider["instance"].topic)
info("Posting changes to " + wf.change_provider["instance"].topic)
crowdnav_instance_number = crowdnav_instance_number + 1
if crowdnav_instance_number == wf.execution_strategy[
"population_size"]:
crowdnav_instance_number = 0
exp["ignore_first_n_results"] = wf.execution_strategy[
"ignore_first_n_results"]
exp["sample_size"] = wf.execution_strategy["sample_size"]
exp["knobs"] = knob_object
# the experiment function returns what the evaluator in definition.py is computing
return experimentFunction(wf, exp)
def start_seq_runtime_stategy(wf):
""" executes all experiments from the definition file """
info("> ExecStrategy | SequentialRuntimeConfigs", Fore.CYAN)
retrieve_true_knobs(wf)
#the regular sequential code starts here
wf.totalExperiments = len(wf.execution_strategy["knobs"])
for kn in wf.execution_strategy["knobs"]:
experimentFunction(wf, {
"knobs":kn,
"ignore_first_n_results": wf.execution_strategy["ignore_first_n_results"],
"sample_size": wf.execution_strategy["sample_size"],
})
def self_optimizer_execution(wf, opti_values, variables):
""" this is the function we call and that returns a value for optimization """
knob_object = recreate_knob_from_optimizer_values(variables, opti_values)
# create a new experiment to run in execution
exp = dict()
exp["ignore_first_n_results"] = wf.execution_strategy[
"ignore_first_n_results"]
exp["sample_size"] = wf.execution_strategy["sample_size"]
exp["knobs"] = knob_object
return experimentFunction(wf, exp)
def arm_experiment(wf, arm):
return experimentFunction(
wf, {
"knobs": {
"config": arm
},
"ignore_first_n_results":
wf.execution_strategy["ignore_first_n_results"],
"sample_size":
wf.execution_strategy["sample_size"]
})
def step_execution(wf, configuration, variables):
""" runs a single step_execution experiment """
knob_object = recreate_knob_from_step_explorer_values(
variables, configuration)
# create a new experiment to run in execution
exp = dict()
exp["ignore_first_n_results"] = wf.execution_strategy[
"ignore_first_n_results"]
exp["sample_size"] = wf.execution_strategy["sample_size"]
exp["knobs"] = knob_object
return experimentFunction(wf, exp)
def effector(wf, action):
print("\n\n\n\n\nEffecting action " + action + "\n\n\n\n\n")
wf.primary_data_provider["last_action"] = action.split()[0]
return experimentFunction(
wf, {
"knobs":
action,
"ignore_first_n_results":
wf.execution_strategy["ignore_first_n_results"],
"sample_size":
wf.execution_strategy["sample_size"]
})