def smoothen(objective_map, decisions, keys=[0, 1]):
smoothened = []
for key in keys:
objectives = objective_map[key]
sk_data = []
for index, (decision, dec_objectives) in enumerate(zip(decisions, objectives)):
sk_data.append([str(index)]+dec_objectives)
ranks = rdivDemo(sk_data, do_print=False)
rank_map = {}
for rank, _, x in ranks:
meds_iqrs = rank_map.get(rank, [[], []])
med, iqr = median_iqr(x.all)
meds_iqrs[0].append(med)
meds_iqrs[1].append(iqr)
rank_map[rank] = meds_iqrs
smooth_objs = {}
meds, iqrs = [], []
for rank, _, x in ranks:
meds_iqrs = rank_map[rank]
med, iqr = round(float(np.mean(meds_iqrs[0])), 2), round(float(np.mean(meds_iqrs[1])), 2)
meds.append(med)
iqrs.append(iqr)
smooth_objs["meds"] = meds
smooth_objs["iqrs"] = iqrs
smoothened.append(smooth_objs)
return smoothened
def scores(self, n=1000, seed_val=None):
"""
Evaluate the graph n times and
return the average value
:param n: Number of evals
:param seed_val: Random seed to be set
:return: The average score
"""
from utilities.sk import rdivDemo
if not seed_val is None:
random.seed(seed_val)
final = []
for _ in xrange(n):
for node in self._tree.nodes:
node.value = None
final.append(self.score())
final = ["Roots"] + final
rdivDemo([final])
def scores(self, n=1000, seed_val=None):
"""
Evaluate the graph n times and
return the average value
:param n: Number of evals
:param seed_val: Random seed to be set
:return: The average score
"""
from utilities.sk import rdivDemo
if not seed_val is None:
random.seed(seed_val)
final = []
for _ in xrange(n):
for node in self._tree.nodes:
node.value = None
final.append(self.score())
final = ["Roots"] + final
rdivDemo([final])
