def process_OOD(file_name):
name = os.path.basename(file_name).split(".")[0]
print("### " + name)
print("```")
model = Model(file_name)
de = DE(model)
stat = de.run()
print("Time Taken : ", stat.runtime)
stat.tiles()
data_map = stat.median_spread()
objs = stat.spit_objectives()
headers = [obj.__name__.split("_")[-1] for obj in de.settings.obj_funcs]
cluster_input = [headers] + objs
print("")
g = KMeans(k=2)
clusters = g.run(cluster_input)
med_spread_plot(data_map, headers, fig_name="img/gen_bin_" + name)
plot_clusters(clusters,
fig_name="img/bin_" + name,
col_names=headers,
s=50,
edgecolors='none')
print("```")
print("" % name)
return stat.runtime
def test_pystar():
from pystar.models.CSServices import graph
from pystar.model import Model
#print(graph.nodes)
model = Model(graph)
de = DE(model)
stat = de.run()
stat.tiles()
lastgen = stat.generations[-1]
maxs = [-1] * len(lastgen[0].objectives)
bests = [None] * len(lastgen[0].objectives)
for point in lastgen:
for i in range(len(maxs)):
if point.objectives[i] > maxs[i]:
maxs[i] = point.objectives[i]
bests[i] = point
print(maxs)
columns = ["id", "type", "value", "is_random", "name"]
stat.tabulate(columns, bests[0])
def test_pystar():
from pystar.models.CSServices import graph
from pystar.model import Model
#print(graph.nodes)
model = Model(graph)
de = DE(model)
stat = de.run()
stat.tiles()
last_gen = stat.generations[-1]
maxs = [-1]*len(last_gen[0].objectives)
bests = [None]*len(last_gen[0].objectives)
for point in last_gen:
for i in range(len(maxs)):
if point.objectives[i] > maxs[i]:
maxs[i] = point.objectives[i]
bests[i] = point
print(maxs)
columns = ["id", "type", "value", "is_random", "name"]
stat.tabulate(columns, bests[0])
def process_ood(file_name):
name = os.path.basename(file_name).split(".")[0]
print("### " + name)
print("```")
model = Model(file_name)
de = DE(model)
stat = de.run()
print("Time Taken : ", stat.runtime)
stat.tiles()
data_map = stat.median_spread()
objs = stat.spit_objectives()
headers = [obj.__name__.split("_")[-1] for obj in de.settings.obj_funcs]
cluster_input = [headers] + objs
print("")
g = KMeans(k=2)
clusters = g.run(cluster_input)
med_spread_plot(data_map, headers, fig_name="img/gen_bin_"+name)
plot_clusters(clusters,
fig_name="img/bin_"+name,
col_names=headers,
s=50, edgecolors='none')
print("```")
print(""%name)
return stat.runtime
def __init__(self, model, **settings): O.__init__(self) self.model = model #self.model.bases.extend(self.get_conflicts());self.model.assign_costs() self.settings = default().update(**settings) self.de = DE(model, gens = self.settings.k1)
class Star1(O):
def __init__(self, model, **settings):
O.__init__(self)
self.model = model
#self.model.bases.extend(self.get_conflicts());self.model.assign_costs()
self.settings = default().update(**settings)
self.de = DE(model, gens = self.settings.k1)
def get_conflicts(self):
model = self.model
graph = model.get_tree()
nodes = []
for node in graph.get_nodes():
if node.type == "softgoal" and len(node.from_edges) > 1:
toggle = None
for edge_id in node.from_edges:
edge = graph.get_edge(edge_id)
if edge.type == "contribution":
temp_toggle = sign(template.Edge.get_contribution_weight(edge.value))
if toggle is None: toggle = temp_toggle
if temp_toggle != toggle:
nodes.append(node)
break
return nodes
def sample(self):
stat = self.de.run()
stat.settings.gen_step = self.settings.gen_step
stat.tiles()
best = set()
for obj_index in range(len(self.de.settings.obj_funcs)):
sorted_pop = sorted(stat.generations[-1], key=lambda x: x.objectives[obj_index], reverse=True)[:len(stat.generations[-1])//5]
best.update(sorted_pop)
rest = set()
for gen in stat.generations:
for point in gen:
if not point in best:
rest.add(point)
return best, rest
# def sample(self):
# stat = self.de.run()
# stat.settings.gen_step = self.settings.gen_step
# stat.tiles()
# population = set()
# for gen in stat.generations:
# for point in gen:
# population.add(point)
# best_size = int(len(population) * self.settings.best_percent/100)
# best = sel_nsga2(self.model, list(population), best_size)
# rest = population - set(best)
# return list(best), list(rest)
def rank(self, best, rest):
best_size = len(best)
rest_size = len(rest)
p_best = best_size / (best_size + rest_size)
p_rest = rest_size / (best_size + rest_size)
decisions = []
for dec_node in self.model.bases:
f_best, pos_count, neg_count = 0, 0, 0
for point in best:
if point.decisions[dec_node.id] > 0:
pos_count += 1
elif point.decisions[dec_node.id] < 0:
neg_count += 1
f_pos_best = pos_count / best_size
l_pos_best = f_pos_best * p_best
f_neg_best = neg_count / best_size
l_neg_best = f_neg_best * p_best
f_pos_rest, f_neg_rest = 0, 0
for point in rest:
if point.decisions[dec_node.id] > 0:
f_pos_rest += 1
else:
f_neg_rest += 1
f_pos_rest /= rest_size
f_neg_rest /= rest_size
l_pos_rest = f_pos_rest * p_rest
l_neg_rest = f_neg_rest * p_rest
if l_pos_best == 0 and l_pos_rest == 0:
sup_pos = 0
else:
sup_pos = l_pos_best ** 2 / (l_pos_best + l_pos_rest)
if l_neg_best == 0 and l_neg_rest == 0:
sup_neg = 0
else:
sup_neg = l_neg_best ** 2 / (l_neg_best + l_neg_rest)
decisions.append(Decision(id = dec_node.id, name = dec_node.name,
support=sup_pos, value = 1,
type = dec_node.type, container=dec_node.container,
cost = self.model.cost_map[dec_node.id]))
decisions.append(Decision(id = dec_node.id, name = dec_node.name,
support=sup_neg, value = -1,
type = dec_node.type, container=dec_node.container,
cost = self.model.cost_map[dec_node.id]))
decisions.sort(key=lambda x:x.support, reverse=True)
sorted_decs = []
aux = set()
for dec in decisions:
if dec.id not in aux:
sorted_decs.append(dec)
aux.add(dec.id)
assert len(sorted_decs) == len(self.model.bases), "Mismatch after sorting support"
return sorted_decs
def generate(self, presets = None):
population = list()
while len(population) < self.settings.k2:
point = Point(self.model.generate())
if not point in population:
for preset in presets:
point.decisions[preset.id] = preset.value
population.append(point)
return population
@staticmethod
def objective_stats(generations):
stats = []
obj_len = len(generations[0][0].objectives)
objective_map={}
for i in range(obj_len):
objectives = []
data_map = {}
meds = []
iqrs = []
for gen, pop in enumerate(generations):
objs = [pt.objectives[i] for pt in pop]
objectives.append(objs)
med, iqr = median_iqr(objs)
meds.append(med)
iqrs.append(iqr)
objective_map[i] = objectives
data_map["meds"] = meds
data_map["iqrs"] = iqrs
stats.append(data_map)
return stats, objective_map
def evaluate(self, point, decisions):
model = self.model
if not point.objectives:
model.reset_nodes(point.decisions)
funcs = [Point.eval_softgoals, Point.eval_goals, Point.eval_paths]
point.objectives = [func(model) for func in funcs]
point.objectives.append(sum(decision.cost for decision in decisions if decision.value > 0))
point._nodes = [node.clone() for node in model.get_tree().get_nodes()]
return point.objectives
def prune(self, support):
gens = []
for i in range(len(support)):
decisions = support[:i]
population = self.generate(decisions)
for point in population:
self.evaluate(point, decisions)
# TODO - Mark Best objective model here
#self.de.settings.evaluation(point, self.model, self.de.settings.obj_funcs)
gens.append(population)
obj_stats, objective_map = self.objective_stats(gens)
return obj_stats, gens, objective_map
def report(self, stats, sub_folder, fig_name):
#headers = [obj.__name__.split("_")[-1] for obj in self.de.settings.obj_funcs]
headers = ["softgoals", "goals", "costs"]
med_spread_plot(stats, headers, fig_name="img/"+sub_folder+"/"+fig_name+".png")
return "img/"+sub_folder+"/"+fig_name+".png"
@staticmethod
def get_elbow(gens, index, obj_index=None):
pop = gens[index]
pop = sorted(pop, key=lambda x: x.objectives[obj_index])
point = pop[len(pop)//2]
return point
def __init__(self, model, **settings):
O.__init__(self)
self.model = model
self.model.bases.extend(self.get_conflicts())
self.settings = default().update(**settings)
self.de = DE(model, gens=self.settings.k1)
class Star1(O):
def __init__(self, model, **settings):
O.__init__(self)
self.model = model
self.model.bases.extend(self.get_conflicts())
self.settings = default().update(**settings)
self.de = DE(model, gens=self.settings.k1)
def get_conflicts(self):
model = self.model
graph = model.get_tree()
nodes = []
for node in graph.get_nodes():
if node.type == "softgoal" and len(node.from_edges) > 1:
toggle = None
for edge_id in node.from_edges:
edge = graph.get_edge(edge_id)
if edge.type == "contribution":
temp_toggle = sign(
template.Edge.get_contribution_weight(edge.value))
if toggle is None: toggle = temp_toggle
if temp_toggle != toggle:
nodes.append(node)
break
return nodes
def sample(self):
stat = self.de.run()
stat.tiles()
best = set()
for obj_index in range(len(self.de.settings.obj_funcs)):
sorted_pop = sorted(stat.generations[-1],
key=lambda x: x.objectives[obj_index],
reverse=True)[:len(stat.generations[-1]) // 5]
best.update(sorted_pop)
rest = set()
for gen in stat.generations:
for point in gen:
if not point in best:
rest.add(point)
return best, rest
def rank(self, best, rest):
best_size = len(best)
rest_size = len(rest)
p_best = best_size / (best_size + rest_size)
p_rest = rest_size / (best_size + rest_size)
sup_map = {}
for dec_node in self.model.bases:
f_best = 0
for point in best:
if point.decisions[dec_node.id] > 0:
f_best += 1
f_best /= best_size
l_best = f_best * p_best
f_rest = 0
for point in rest:
if point.decisions[dec_node.id] > 0:
f_rest += 1
f_rest /= best_size
l_rest = f_rest * p_rest
sup_best = l_best**2 / (l_best + l_rest)
sup_map[dec_node.id] = sup_best
return sup_map
def generate(self, presets=None):
population = list()
while len(population) < self.de.settings.candidates:
point = Point(self.model.generate())
if not point in population:
population.append(point)
for point in population:
for preset in presets:
point.decisions[preset] = 1
return population
def objective_stats(self, generations):
stats = []
for i in range(len(self.de.settings.obj_funcs)):
data_map = {}
meds = []
iqrs = []
for gen, pop in enumerate(generations):
objs = [pt.objectives[i] for pt in pop]
med, iqr = median_iqr(objs)
meds.append(med)
iqrs.append(iqr)
data_map["meds"] = meds
data_map["iqrs"] = iqrs
stats.append(data_map)
return stats
def prune(self, support):
gens = []
for i in range(len(support)):
sup_keys = support[:i]
population = self.generate(sup_keys)
for point in population:
self.de.settings.evaluation(point, self.model,
self.de.settings.obj_funcs)
gens.append(population)
obj_stats = self.objective_stats(gens)
return obj_stats
def report(self, stats):
headers = [
obj.__name__.split("_")[-1] for obj in self.de.settings.obj_funcs
]
med_spread_plot(stats,
headers,
fig_name="img/costs/" + self.model.get_tree().name +
".png")
class Star1(O):
def __init__(self, model, **settings):
O.__init__(self)
self.model = model
#self.model.bases.extend(self.get_conflicts());self.model.assign_costs()
self.settings = default().update(**settings)
self.de = DE(model, gens = self.settings.k1)
def get_conflicts(self):
model = self.model
graph = model.get_tree()
nodes = []
for node in graph.get_nodes():
if node.type == "softgoal" and len(node.from_edges) > 1:
toggle = None
for edge_id in node.from_edges:
edge = graph.get_edge(edge_id)
if edge.type == "contribution":
temp_toggle = sign(template.Edge.get_contribution_weight(edge.value))
if toggle is None: toggle = temp_toggle
if temp_toggle != toggle:
nodes.append(node)
break
return nodes
def sample(self):
stat = self.de.run()
stat.settings.gen_step = self.settings.gen_step
stat.tiles()
best = set()
for obj_index in range(len(self.de.settings.obj_funcs)):
sorted_pop = sorted(stat.generations[-1], key=lambda x: x.objectives[obj_index], reverse=True)[:len(stat.generations[-1])//5]
best.update(sorted_pop)
rest = set()
for gen in stat.generations:
for point in gen:
if not point in best:
rest.add(point)
return best, rest
# def sample(self):
# stat = self.de.run()
# stat.settings.gen_step = self.settings.gen_step
# stat.tiles()
# population = set()
# for gen in stat.generations:
# for point in gen:
# population.add(point)
# best_size = int(len(population) * self.settings.best_percent/100)
# best = sel_nsga2(self.model, list(population), best_size)
# rest = population - set(best)
# return list(best), list(rest)
def rank(self, best, rest):
best_size = len(best)
rest_size = len(rest)
p_best = best_size / (best_size + rest_size)
p_rest = rest_size / (best_size + rest_size)
decisions = []
for dec_node in self.model.bases:
f_best, pos_count, neg_count = 0, 0, 0
for point in best:
if point.decisions[dec_node.id] > 0:
pos_count += 1
elif point.decisions[dec_node.id] < 0:
neg_count += 1
f_pos_best = pos_count / best_size
l_pos_best = f_pos_best * p_best
f_neg_best = neg_count / best_size
l_neg_best = f_neg_best * p_best
f_pos_rest, f_neg_rest = 0, 0
for point in rest:
if point.decisions[dec_node.id] > 0:
f_pos_rest += 1
else:
f_neg_rest += 1
f_pos_rest /= rest_size
f_neg_rest /= rest_size
l_pos_rest = f_pos_rest * p_rest
l_neg_rest = f_neg_rest * p_rest
if l_pos_best == 0 and l_pos_rest == 0:
sup_pos = 0
else:
sup_pos = l_pos_best ** 2 / (l_pos_best + l_pos_rest)
if l_neg_best == 0 and l_neg_rest == 0:
sup_neg = 0
else:
sup_neg = l_neg_best ** 2 / (l_neg_best + l_neg_rest)
decisions.append(Decision(id = dec_node.id, name = dec_node.name,
support=sup_pos, value = 1,
type = dec_node.type, container=dec_node.container,
cost = self.model.cost_map[dec_node.id]))
decisions.append(Decision(id = dec_node.id, name = dec_node.name,
support=sup_neg, value = -1,
type = dec_node.type, container=dec_node.container,
cost = self.model.cost_map[dec_node.id]))
decisions.sort(key=lambda x:x.support, reverse=True)
sorted_decs = []
aux = set()
for dec in decisions:
if dec.id not in aux:
sorted_decs.append(dec)
aux.add(dec.id)
assert len(sorted_decs) == len(self.model.bases), "Mismatch after sorting support"
return sorted_decs
def generate(self, presets = None):
population = list()
while len(population) < self.settings.k2:
point = Point(self.model.generate())
if not point in population:
for preset in presets:
point.decisions[preset.id] = preset.value
population.append(point)
return population
@staticmethod
def objective_stats(generations):
stats = []
obj_len = len(generations[0][0].objectives)
objective_map={}
for i in range(obj_len):
objectives = []
data_map = {}
meds = []
iqrs = []
for gen, pop in enumerate(generations):
objs = [pt.objectives[i] for pt in pop]
objectives.append(objs)
med, iqr = median_iqr(objs)
meds.append(med)
iqrs.append(iqr)
objective_map[i] = objectives
data_map["meds"] = meds
data_map["iqrs"] = iqrs
stats.append(data_map)
return stats, objective_map
def evaluate(self, point, decisions):
model = self.model
if not point.objectives:
model.reset_nodes(point.decisions)
funcs = [Point.eval_softgoals, Point.eval_goals, Point.eval_paths]
point.objectives = [func(model) for func in funcs]
point.objectives.append(sum(decision.cost for decision in decisions if decision.value > 0))
point._nodes = [node.clone() for node in model.get_tree().get_nodes()]
return point.objectives
def prune(self, support):
gens = []
for i in range(len(support)):
decisions = support[:i]
population = self.generate(decisions)
for point in population:
self.evaluate(point, decisions)
# TODO - Mark Best objective model here
#self.de.settings.evaluation(point, self.model, self.de.settings.obj_funcs)
gens.append(population)
obj_stats, objective_map = self.objective_stats(gens)
return obj_stats, gens, objective_map
def report(self, stats, sub_folder, fig_name):
#headers = [obj.__name__.split("_")[-1] for obj in self.de.settings.obj_funcs]
headers = ["softgoals", "goals", "costs"]
med_spread_plot(stats, headers, fig_name="img/"+sub_folder+"/"+fig_name+".png")
return "img/"+sub_folder+"/"+fig_name+".png"
@staticmethod
def get_elbow(gens, index, obj_index=None):
pop = gens[index]
pop = sorted(pop, key=lambda x: x.objectives[obj_index])
point = pop[len(pop)//2]
return point
