def process_node(node_i, level, loss, predictions, cur_lvl, top_k, alpha,
loss_type, w, debug, enumerator):
cur_enum_nodes = []
for node_j in level:
if enumerator == "join":
flag = approved_join_slice(node_i, node_j, cur_lvl)
else:
flag = approved_union_slice(node_i, node_j)
if flag and int(node_i.name.split("&&")[0]) < int(
node_j.name.split("&&")[0]):
new_node = SparkNode(loss, predictions)
parents_set = set(new_node.parents)
parents_set.add(node_i)
parents_set.add(node_j)
new_node.parents = list(parents_set)
parent1_attr = node_i.attributes
parent2_attr = node_j.attributes
new_node_attr = union(parent1_attr, parent2_attr)
new_node.attributes = new_node_attr
new_node.name = new_node.make_name()
new_node.key = new_node.make_key()
new_node.calc_bounds(cur_lvl, w)
to_slice = new_node.check_bounds(top_k, len(predictions), alpha)
if to_slice:
new_node.process_slice(loss_type)
new_node.score = opt_fun(new_node.loss, new_node.size, loss,
len(predictions), w)
if new_node.check_constraint(top_k, len(predictions), alpha):
cur_enum_nodes.append(new_node)
if debug:
new_node.print_debug(top_k, cur_lvl)
return cur_enum_nodes
def join_enum_fun(node_a, list_b, predictions, f_l2, debug, alpha, w,
loss_type, cur_lvl, top_k):
x_size = len(predictions)
nodes = []
for node_i in range(len(list_b)):
flag = spark_utils.approved_join_slice(node_i, node_a, cur_lvl)
if not flag:
new_node = SparkNode(predictions, f_l2)
parents_set = set(new_node.parents)
parents_set.add(node_i)
parents_set.add(node_a)
new_node.parents = list(parents_set)
parent1_attr = node_a.attributes
parent2_attr = list_b[node_i].attributes
new_node_attr = union(parent1_attr, parent2_attr)
new_node.attributes = new_node_attr
new_node.name = new_node.make_name()
new_node.calc_bounds(cur_lvl, w)
# check if concrete data should be extracted or not (only for those that have score upper
# and if size of subset is big enough
to_slice = new_node.check_bounds(top_k, x_size, alpha)
if to_slice:
new_node.process_slice(loss_type)
new_node.score = opt_fun(new_node.loss, new_node.size, f_l2,
x_size, w)
# we decide to add node to current level nodes (in order to make new combinations
# on the next one or not basing on its score value
if new_node.check_constraint(
top_k, x_size,
alpha) and new_node.key not in top_k.keys:
top_k.add_new_top_slice(new_node)
nodes.append(new_node)
if debug:
new_node.print_debug(top_k, cur_lvl)
return nodes
def make_first_level(features, predictions, loss, top_k, w, loss_type):
first_level = []
# First level slices are enumerated in a "classic way" (getting data and not analyzing bounds
for feature in features:
new_node = SparkNode(loss, predictions)
new_node.parents = [feature]
new_node.attributes.append(feature)
new_node.name = new_node.make_name()
new_node.key = new_node.make_key()
new_node.process_slice(loss_type)
new_node.score = opt_fun(new_node.loss, new_node.size, loss,
len(predictions), w)
new_node.c_upper = new_node.score
first_level.append(new_node)
new_node.print_debug(top_k, 0)
return first_level