def execute_in_bicases_constraints(self, task):
# adapt beam width to the result set size if desired
if self.beamWidthAdaptive:
self.beamWidth = task.resultSetSize[0]
# check if beam size is to small for result set
if (self.beamWidth < task.resultSetSize[0]):
raise RuntimeError(
'Beam width in the beam search algorithm is smaller than the result set size!'
)
# init
beam = [(0, Subgroup(task.target, []), Subgroup(task.target, []))]
beam2 = [(0, Subgroup(task.target, []))]
last_beam = None
depth1 = 0
depth2 = 0
while beam != last_beam and depth1 < task.depth:
last_beam = beam.copy()
last_candidates = [pattern[1] for pattern in last_beam]
all_sg1_candidates = last_candidates
# a list contains all sg1 without duplicates
all_sg1_candidates = [
i for n, i in enumerate(last_candidates)
if i not in last_candidates[:n]
]
# print(all_sg1_candidates)
for last_sg1 in all_sg1_candidates:
for sel1 in task.searchSpace:
new_selectors1 = list(
last_sg1.subgroupDescription.selectors)
if not sel1 in new_selectors1:
new_selectors1.append(sel1)
sg1 = Subgroup(task.target, new_selectors1)
# print('sg1')
# print(sg1)
if sg1.count(task.data) != 0:
beam2 = [(0, Subgroup(task.target, []))]
depth2 = 0
last_beam2 = None
searchSpace_sg2_1 = []
ignore_attrs = []
for sel in new_selectors1:
ignore_attr_name = sel.getAttributeName()
searchSpace_sg2_1.extend(createNominalSelectorsForAttribute(task.data, \
ignore_attr_name))
# searchSpace_sg2_1.extend(createNumericSelectorForAttribute(task.data, \
# ignore_attr_name))
ignore_attrs.append(ignore_attr_name)
searchSpace_sg2_1.remove(sel)
searchSpace_sg2_left = [
i for i in task.searchSpace
if i.getAttributeName() not in ignore_attrs
]
map_searchSpace_sg2 = {}
map_searchSpace_sg2[0] = searchSpace_sg2_1
for i in range(1, task.depth):
map_searchSpace_sg2[i] = searchSpace_sg2_left
# print(map_searchSpace_sg2)
while beam2 != last_beam2 and depth2 < task.depth:
last_beam2 = beam2.copy()
for (_, last_sg2) in last_beam2:
if depth2 == 0 or list(
last_sg2.subgroupDescription.
selectors) != []:
# print(last_sg2)
for sel2 in map_searchSpace_sg2[
depth2]:
new_selectors2 = list(
last_sg2.subgroupDescription.
selectors)
new_selectors2.append(sel2)
# if new_selectors2 != new_selectors1:
sg2 = Subgroup(
task.target, new_selectors2)
quality = task.qf.evaluateFromDataset_BiCases(
task.data, sg1, sg2)
# print(quality)
ut.addIfRequired(
beam2,
sg2,
quality,
task,
check_for_duplicates=True)
# print(beam2)
depth2 += 1
beam2.sort(key=lambda x: x[0], reverse=True)
# print(beam2)
for i in range(len(beam2)):
ut.addIfRequired_Bicases(
beam,
sg1,
beam2[i][-1],
beam2[i][0],
task,
check_for_duplicates=True)
# print('here')
# for i in range(len(beam)):
# print(beam[i][1])
depth1 += 1
result = beam[:]
result.sort(key=lambda x: x[0], reverse=True)
return result
def execute_in_bicases(self, task):
# adapt beam width to the result set size if desired
if self.beamWidthAdaptive:
self.beamWidth = task.resultSetSize[0]
# check if beam size is to small for result set
if (self.beamWidth < task.resultSetSize[0]):
raise RuntimeError(
'Beam width in the beam search algorithm is smaller than the result set size!'
)
# init
beam = [(0, Subgroup(task.target, []), Subgroup(task.target, []))]
beam2 = [(0, Subgroup(task.target, []))]
last_beam = None
depth1 = 0
depth2 = 0
while beam != last_beam and depth1 < task.depth:
last_beam = beam.copy()
last_candidates = [pattern[1] for pattern in last_beam]
all_sg1_candidates = last_candidates
# a list contains all sg1 without duplicates
all_sg1_candidates = [
i for n, i in enumerate(last_candidates)
if i not in last_candidates[:n]
]
# print(all_sg1_candidates)
for last_sg1 in all_sg1_candidates:
for sel1 in task.searchSpace:
new_selectors1 = list(
last_sg1.subgroupDescription.selectors)
if not sel1 in new_selectors1:
new_selectors1.append(sel1)
sg1 = Subgroup(task.target, new_selectors1)
# print('sg1')
# print(sg1)
if sg1.count(task.data) != 0:
beam2 = [(0, Subgroup(task.target, []))]
depth2 = 0
last_beam2 = None
searchSpace_sg2 = [
i for i in task.searchSpace
if i not in new_selectors1
]
while beam2 != last_beam2 and depth2 < task.depth:
last_beam2 = beam2.copy()
for (_, last_sg2) in last_beam2:
for sel2 in searchSpace_sg2:
new_selectors2 = list(
last_sg2.subgroupDescription.
selectors)
if not sel2 in new_selectors2:
new_selectors2.append(sel2)
# if new_selectors2 != new_selectors1:
sg2 = Subgroup(
task.target, new_selectors2)
quality = task.qf.evaluateFromDataset_BiCases(
task.data, sg1, sg2)
ut.addIfRequired(
beam2,
sg2,
quality,
task,
check_for_duplicates=True)
depth2 += 1
beam2.sort(key=lambda x: x[0], reverse=True)
# print(beam2)
for i in range(len(beam2)):
ut.addIfRequired_Bicases(
beam,
sg1,
beam2[i][-1],
beam2[i][0],
task,
check_for_duplicates=True)
# print('here')
# for i in range(len(beam)):
# print(beam[i][1])
depth1 += 1
result = beam[:]
result.sort(key=lambda x: x[0], reverse=True)
return result