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fitness_evaluator.py
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fitness_evaluator.py
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import numpy as np
from shapely.geometry import Point, MultiPolygon
from shapely.ops import cascaded_union
class Evaluator(object):
def __init__(self, parametric=False):
self._parametric=parametric
def _exploration_fit(self, case, return_components = False):
if case.agents[0].sensors.get("Coverage") is None:
if return_components:
return {}
else:
return 0.
squares = []
coverage = 0.
for i in xrange(10):
for j in xrange(10):
squares.append(case.blackboard["Coverage"][i,j])
if case.blackboard["Coverage"][i,j] > 0:
coverage += min(2., float(case.blackboard["Coverage"][i,j]))
coverage = float(coverage)/200.
squares = sorted(squares)
num_agents = len(case.agents)
max_velocity = case.agents[0].platform.max_velocity
max_time = case.config["config_simulator"]["max_time"]
max_squares = float(num_agents * max_velocity * max_time) / (100.)
max_square_median = max_squares/ (10.*10.)
median_frequency_normalized = float(squares[len(squares)/2]) / max_square_median
if return_components:
components = {}
components["median"] = median_frequency_normalized
return components
else:
fitness = median_frequency + coverage
return fitness
def _localization_fit(self, case, return_components = False):
if case.agents[0].sensors.get("Localization") is None:
if return_components:
return {}
else:
return 0.
location_estimates = []
for agent in case.agents:
location_estimates.extend(agent.sensors.get("Localization").history)
location_estimate_variance = 0.
actual_position = np.array([200.,200.])
for estimate in location_estimates:
distance = np.linalg.norm(estimate-actual_position)
location_estimate_variance += distance
location_estimate_variance = location_estimate_variance/len(location_estimates)
if return_components:
components = {}
components["variance"] = 1./location_estimate_variance
return components
else:
fitness = 1./location_estimate_variance
return fitness
def _network_fit(self, case1, case2, return_components = False):
if case1.agents[0].sensors.get("Relay") is None:
if return_components:
return {}
else:
return 0.
covered_area_avg = 0.
for case in [case1,case2]:
connection_sets = []
for agent in case.agents:
member_of = []
for i,connection_group in enumerate(connection_sets):
connected_to = False
for connected_agent in agent.sensors.get("Relay").connections:
if connected_agent in connection_group:
connected_to = True
if connected_to:
member_of.append((i,connection_group))
if len(member_of) == 0:
new_set = set()
new_set.add(agent)
connection_sets.append(new_set)
elif len(member_of) == 1:
member_of[0][1].add(agent)
else:
new_set = set()
for i,connection_group in member_of:
new_set.update(connection_group)
member_of.reverse()
for i,_ in member_of:
connection_sets.pop(i)
connection_sets.append(new_set)
largest_set = max(connection_sets, key=lambda s: len(s))
polygons = []
com_range = 0.
for agent in largest_set:
x,y = agent.platform.position
com_range = agent.sensors.get("Relay")._range
p = Point(x,y).buffer(com_range)
polygons.append(p)
covered_area = cascaded_union(MultiPolygon(polygons)).area
max_covered_area = (len(case.agents)*3.14*com_range*com_range)/2.0
covered_area_percentage = covered_area / max_covered_area
covered_area_avg += covered_area_percentage
if return_components:
components = {}
components["covered"] = covered_area_avg/2.0
return components
else:
raise Exception("Not properly implemented")
fitness = float(len(largest_set))/len(case.agents)
return fitness
def _base_fit(self, case1, case2, return_components=False):
movement = 0.
for agent_start, agent_end in zip(case1.agents, case2.agents):
distance = np.linalg.norm(agent_start.platform.position-agent_end.platform.position)
movement += min(1., distance/100.)
if return_components:
components = {}
components["movement"] = movement/len(case1.agents)
return components
else:
fitness = movement/len(case1.agents)
return 1. + fitness
def post_evaluator_loggers(self, list_of_loggers):
summed = 1.
for logger in list_of_loggers:
with logger as open_logger:
summed *= self._case_evaluator(open_logger._simulation_log)
return summed
def post_evaluator_shelves(self, list_of_shelves):
summed = 1.
for shelve_t in list_of_shelves:
summed *= self._case_evaluator(shelve_t)
return summed
def _case_evaluator(self, run_log):
ticks = sorted(map(int,run_log.keys()))
start_case_id = str(min(ticks))
end_case_id = str(max(ticks))
start_case = run_log[start_case_id]
end_case = run_log[end_case_id]
fit_base = self._base_fit(start_case, end_case)
fit_net = self._network_fit(end_case)
fit_loc = self._localization_fit(end_case)
fit_exp = self._exploration_fit(end_case)
fitness = fit_base + fit_net + fit_loc + fit_exp
return fitness
def fitness_components(self, list_of_shelves):
components = {}
for shelve_t in list_of_shelves:
components[case_name] = self._case_components(shelve_t)
return components
def _case_components(self, shelve):
ticks = sorted(map(int,shelve.keys()))
start_case_id = str(min(ticks))
middle_case_id = str((max(ticks)-min(ticks))/2)
end_case_id = str(max(ticks))
start_case = shelve[start_case_id]
middle_case = shelve[middle_case_id]
end_case = shelve[end_case_id]
case_name = str(start_case)
case_component = {}
#case_component["base"] = self._base_fit(start_case, end_case, return_components=True)
case_component["network"] = self._network_fit(middle_case, end_case, return_components=True)
#case_component["localization"] = self._localization_fit(end_case, return_components=True)
case_component["exploration"] = self._exploration_fit(end_case, return_components=True)
return case_component
def fitness_map_elites(self, list_of_loggers):
#test_fitness = 1.
test_fitness = 0.
import numpy as np
characteristics = {}
for logger in list_of_loggers:
with logger as open_logger:
case_components = self._case_components(open_logger._simulation_log)
for application, application_component in case_components.items():
for measure, value in application_component.items():
characteristics_name = "_".join([application,measure])
if characteristics.get(characteristics_name) is None:
characteristics[characteristics_name] = value
else:
characteristics[characteristics_name] += value
if not self._parametric:
test_fitness += 2./(1.+np.linalg.norm(open_logger._simulation_log['0'].config["platform_templates"]["adv"]["config_behavior"]["weights"]))
else:
test_fitness += 2./(1.+np.linalg.norm(open_logger._simulation_log['0'].config["platform_templates"]["adv"]["config_behavior"]["weights"])+np.linalg.norm(open_logger._simulation_log['0'].config["platform_templates"]["adv"]["config_behavior"]["scale"]))
for key,value in characteristics.items():
characteristics[key] = value/len(list_of_loggers)
test_fitness = test_fitness/len(list_of_loggers)
return test_fitness, characteristics
if __name__=="__main__":
import shelve, cPickle
import argparse
shelve.Pickler = cPickle.Pickler
shelve.Unpickler = cPickle.Unpickler
def create_parser():
parser = argparse.ArgumentParser()
parser.add_argument("filename", nargs=1, type=str)
return parser
parser = create_parser()
args = parser.parse_args()
eva = Evaluator()
cases = shelve.open(args.filename[0])
print eva._case_components(cases)