def __init__(self, model, **settings):
"""
Initialize the Active Learner
:param model:
:return:
"""
O.__init__(self)
self.de = DE(model)
self.settings = default_settings().update(**settings)
def __init__(self, model, **settings): """ Initialize the Active Learner :param model: :return: """ O.__init__(self) self.de = DE(model) self.settings = default_settings().update(**settings)
class Active(O):
"""
Identifying the importance of decisions
"""
def __init__(self, model, **settings):
"""
Initialize the Active Learner
:param model:
:return:
"""
O.__init__(self)
self.de = DE(model)
self.settings = default_settings().update(**settings)
def learn(self):
best_points = []
for _ in range(self.settings.gens):
say(".")
self.de.model.generate_costs_benefits()
stat = self.de.run()
last_gen = stat.generations[-1]
gen_best_costs = set()
gen_best_benefits = set()
best_cost = sys.maxint
best_benefit = -sys.maxint
for point in last_gen:
if point.objectives[1] > best_benefit:
gen_best_benefits = set()
gen_best_benefits.add(point)
elif point.objectives[1] == best_benefit:
gen_best_benefits.add(point)
if point.objectives[0] < best_cost:
gen_best_costs = set()
gen_best_costs.add(point)
elif point.objectives[0] == best_cost:
gen_best_costs.add(point)
best_points += list(gen_best_costs)
best_points += list(gen_best_benefits)
return best_points
def cluster(self, best_points):
rows = []
for point in best_points:
objs = point.objectives + [point.decisions_set]
row = Row(objs)
row.meta = O(costs=point.node_costs, benefits=point.node_benefits)
rows.append(row)
where = Where(rows, min_size=(len(rows)**0.5))
print("")
root = where.cluster(verbose=self.settings.verbose)
clusters = where.get_leaves(root)
return clusters
def report_cluster(self, cluster):
decisions_map = {}
costs_map = {}
benefits_map = {}
report = Report()
for row in cluster.get_rows():
for index, decision in enumerate(row.decisions):
decisions = decisions_map.get(index, [])
decisions.append(decision)
decisions_map[index] = decisions
for node_id in row.meta.costs.keys():
node_costs = costs_map.get(node_id, [])
node_costs.append(row.meta.costs[node_id])
costs_map[node_id] = node_costs
node_benefits = benefits_map.get(node_id, [])
node_benefits.append(row.meta.benefits[node_id])
benefits_map[node_id] = node_benefits
decisions_report = OrderedDict()
for index, name in enumerate(self.settings.decisions_names):
median, iqr = median_iqr(decisions_map[index])
decisions_report[name] = O(median=median, iqr=iqr)
report.decisions = decisions_report
nodes_report = OrderedDict()
for node_id in costs_map.keys():
name = self.de.model.get_tree().get_node(node_id).name
costs = costs_map[node_id]
benefits = benefits_map[node_id]
costs_median, costs_iqr = median_iqr(costs)
benefits_median, benefits_iqr = median_iqr(benefits)
nodes_report[name] = O(cost=O(median=costs_median, iqr=costs_iqr),
benefit=O(median=benefits_median,
iqr=benefits_iqr))
report.nodes = nodes_report
return report
def dump_clusters(self, cluster_reports, folder):
def to_csv(file_name, rows):
with open(file_name, "wb") as file_obj:
writer = csv.writer(file_obj)
writer.writerows(rows)
return file_name
#decisions = cluster_reports[0].nodes.keys()
decisions = [base.name for base in self.de.model.bases]
objectives = cluster_reports[0].decisions.keys()
costs_list, benefits_list = [], []
header = ["Cluster ID"
] + decisions + ["?%s" % obj for obj in objectives]
costs_list.append(header)
benefits_list.append(header)
for cluster_id, cluster in enumerate(cluster_reports):
cost_row, benefit_row = [cluster_id + 1], [cluster_id + 1]
for decision in decisions:
cost_row.append(cluster.nodes[decision].cost.median)
benefit_row.append(cluster.nodes[decision].benefit.median)
for objective in objectives:
cost_row.append(cluster.decisions[objective].median)
benefit_row.append(cluster.decisions[objective].median)
costs_list.append(cost_row)
benefits_list.append(benefit_row)
directory = "csv/%s" % folder
mkdir(directory)
cost_csv = to_csv("%s/costs.csv" % directory, costs_list)
benefit_csv = to_csv("%s/benefits.csv" % directory, benefits_list)
print("### [Costs](../../../src/%s)" % cost_csv)
print("### [Benefits](../../../src/%s)" % benefit_csv)
class Active(O):
"""
Identifying the importance of decisions
"""
def __init__(self, model, **settings):
"""
Initialize the Active Learner
:param model:
:return:
"""
O.__init__(self)
self.de = DE(model)
self.settings = default_settings().update(**settings)
def learn(self):
best_points = []
for _ in range(self.settings.gens):
say(".")
self.de.model.generate_costs_benefits()
stat = self.de.run()
last_gen = stat.generations[-1]
gen_best_costs = set()
gen_best_benefits = set()
best_cost = sys.maxint
best_benefit = -sys.maxint
for point in last_gen:
if point.objectives[1] > best_benefit:
gen_best_benefits = set()
gen_best_benefits.add(point)
elif point.objectives[1] == best_benefit:
gen_best_benefits.add(point)
if point.objectives[0] < best_cost:
gen_best_costs = set()
gen_best_costs.add(point)
elif point.objectives[0] == best_cost:
gen_best_costs.add(point)
best_points += list(gen_best_costs)
best_points += list(gen_best_benefits)
return best_points
def cluster(self, best_points):
rows = []
for point in best_points:
objs = point.objectives + [point.decisions_set]
row = Row(objs)
row.meta = O(costs=point.node_costs, benefits = point.node_benefits)
rows.append(row)
where = Where(rows, min_size = (len(rows)**0.5))
print("")
root = where.cluster(verbose=self.settings.verbose)
clusters = where.get_leaves(root)
return clusters
def report_cluster(self, cluster):
decisions_map = {}
costs_map = {}
benefits_map = {}
report = Report()
for row in cluster.get_rows():
for index, decision in enumerate(row.decisions):
decisions = decisions_map.get(index, [])
decisions.append(decision)
decisions_map[index] = decisions
for node_id in row.meta.costs.keys():
node_costs = costs_map.get(node_id, [])
node_costs.append(row.meta.costs[node_id])
costs_map[node_id] = node_costs
node_benefits = benefits_map.get(node_id, [])
node_benefits.append(row.meta.benefits[node_id])
benefits_map[node_id] = node_benefits
decisions_report = OrderedDict()
for index, name in enumerate(self.settings.decisions_names):
median, iqr = median_iqr(decisions_map[index])
decisions_report[name] = O(median = median, iqr = iqr)
report.decisions = decisions_report
nodes_report = OrderedDict()
for node_id in costs_map.keys():
name = self.de.model.get_tree().get_node(node_id).name
costs = costs_map[node_id]
benefits = benefits_map[node_id]
costs_median, costs_iqr = median_iqr(costs)
benefits_median, benefits_iqr = median_iqr(benefits)
nodes_report[name] = O(cost=O(median=costs_median, iqr=costs_iqr),
benefit=O(median=benefits_median, iqr=benefits_iqr))
report.nodes = nodes_report
return report
def dump_clusters(self, cluster_reports, folder):
def to_csv(file_name, rows):
with open(file_name, "wb") as file_obj:
writer = csv.writer(file_obj)
writer.writerows(rows)
return file_name
#decisions = cluster_reports[0].nodes.keys()
decisions = [base.name for base in self.de.model.bases]
objectives = cluster_reports[0].decisions.keys()
costs_list, benefits_list = [], []
header = ["Cluster ID"] + decisions + ["?%s"%obj for obj in objectives]
costs_list.append(header)
benefits_list.append(header)
for cluster_id, cluster in enumerate(cluster_reports):
cost_row, benefit_row = [cluster_id+1], [cluster_id+1]
for decision in decisions:
cost_row.append(cluster.nodes[decision].cost.median)
benefit_row.append(cluster.nodes[decision].benefit.median)
for objective in objectives:
cost_row.append(cluster.decisions[objective].median)
benefit_row.append(cluster.decisions[objective].median)
costs_list.append(cost_row)
benefits_list.append(benefit_row)
directory = "csv/%s"%folder
mkdir(directory)
cost_csv = to_csv("%s/costs.csv"%directory, costs_list)
benefit_csv = to_csv("%s/benefits.csv"%directory, benefits_list)
print("### [Costs](../../../src/%s)"%cost_csv)
print("### [Benefits](../../../src/%s)"%benefit_csv)
