def searchInternal(self, task, prefix, modificationSet, result, bitset):
sg = Subgroup(task.target, copy.copy(prefix))
sgSize = len(bitset)
positiveInstances = ut.intersect_of_ordered_list(
bitset, self.targetBitset)
sgPositiveCount = len(positiveInstances)
optimisticEstimate = task.qf.optimisticEstimateFromStatistics(
self.popSize, self.popPositives, sgSize, sgPositiveCount)
if (optimisticEstimate <= ut.minimumRequiredQuality(result, task)):
return result
quality = task.qf.evaluateFromStatistics(self.popSize,
self.popPositives, sgSize,
sgPositiveCount)
ut.addIfRequired(result, sg, quality, task)
if (len(prefix) < task.depth):
newModificationSet = copy.copy(modificationSet)
for sel in modificationSet:
prefix.append(sel)
newBitset = ut.intersect_of_ordered_list(
bitset, self.bitsets[sel])
newModificationSet.pop(0)
self.searchInternal(task, prefix, newModificationSet, result,
newBitset)
# remove the sel again
prefix.pop(-1)
return result
def searchInternal(self, task: SubgroupDiscoveryTask, prefix: List,
modificationSet: List, result: List,
useOptimisticEstimates: bool) -> List:
sg = Subgroup(task.target, SubgroupDescription(copy.copy(prefix)))
optimisticEstimate = float("inf")
if useOptimisticEstimates and len(prefix) < task.depth and isinstance(
task.qf, m.BoundedInterestingnessMeasure):
optimisticEstimate = task.qf.optimisticEstimateFromDataset(
task.data, sg)
if (optimisticEstimate <= ut.minimumRequiredQuality(result, task)):
return result
if task.qf.supportsWeights():
quality = task.qf.evaluateFromDataset(task.data, sg,
task.weightingAttribute)
else:
quality = task.qf.evaluateFromDataset(task.data, sg)
ut.addIfRequired(result, sg, quality, task)
if (len(prefix) < task.depth):
newModificationSet = copy.copy(modificationSet)
for sel in modificationSet:
prefix.append(sel)
newModificationSet.pop(0)
self.searchInternal(task, prefix, newModificationSet, result,
useOptimisticEstimates)
# remove the sel again
prefix.pop(-1)
return result
def execute(self, task):
result = []
queue = []
measure_statistics_based = hasattr(task.qf,
'optimisticEstimateFromStatistics')
# init the first level
for sel in task.searchSpace:
queue.append((float("-inf"), [sel]))
while (queue):
q, candidate_description = heappop(queue)
q = -q
if (q) < ut.minimumRequiredQuality(result, task):
break
sg = Subgroup(task.target, candidate_description)
if (measure_statistics_based):
statistics = sg.get_base_statistics(task.data)
ut.addIfRequired(result, sg,
task.qf.evaluateFromStatistics(*statistics),
task)
optimistic_estimate = task.qf.optimisticEstimateFromStatistics(
*statistics) if isinstance(
task.qf,
m.BoundedInterestingnessMeasure) else float("inf")
else:
ut.addIfRequired(result, sg,
task.qf.evaluateFromDataset(task.data, sg),
task)
optimistic_estimate = task.qf.optimisticEstimateFromDataset(
task.data, sg) if isinstance(
task.qf,
m.BoundedInterestingnessMeasure) else float("inf")
# compute refinements and fill the queue
if (len(candidate_description) < task.depth
and optimistic_estimate >= ut.minimumRequiredQuality(
result, task)):
# iterate over all selectors that are behind the last selector contained in the evaluated candidate according to the initial order
index_of_last_selector = min(
task.searchSpace.index(candidate_description[-1]),
len(task.searchSpace) - 1)
for sel in islice(task.searchSpace, index_of_last_selector + 1,
None):
new_description = candidate_description + [sel]
heappush(queue, (-optimistic_estimate, new_description))
result.sort(key=lambda x: x[0], reverse=True)
return result
def execute(self, task):
# adapt beam width to the result set size if desired
if self.beamWidthAdaptive:
self.beamWidth = task.resultSetSize
# check if beam size is to small for result set
if (self.beamWidth < task.resultSetSize):
raise RuntimeError(
'Beam width in the beam search algorithm is smaller than the result set size!'
)
# init
beam = [(0, Subgroup(task.target, []))]
last_beam = None
depth = 0
while (beam != last_beam and depth < task.depth):
last_beam = beam.copy()
for (_, last_sg) in last_beam:
for sel in task.searchSpace:
# create a clone
new_selectors = list(last_sg.subgroupDescription.selectors)
if not sel in new_selectors:
new_selectors.append(sel)
sg = Subgroup(task.target, new_selectors)
quality = task.qf.evaluateFromDataset(task.data, sg)
ut.addIfRequired(beam,
sg,
quality,
task,
check_for_duplicates=True)
depth += 1
result = beam[:task.resultSetSize]
result.sort(key=lambda x: x[0], reverse=True)
return result
def execute(self, task):
measure_statistics_based = hasattr(task.qf,
'optimisticEstimateFromStatistics')
result = []
# init the first level
next_level_candidates = []
for sel in task.searchSpace:
next_level_candidates.append(Subgroup(task.target, [sel]))
# level-wise search
depth = 1
while (next_level_candidates):
# check sgs from the last level
promising_candidates = []
for sg in next_level_candidates:
if (measure_statistics_based):
statistics = sg.get_base_statistics(task.data)
ut.addIfRequired(
result, sg,
task.qf.evaluateFromStatistics(*statistics), task)
optimistic_estimate = task.qf.optimisticEstimateFromStatistics(
*statistics) if isinstance(
task.qf,
m.BoundedInterestingnessMeasure) else float("inf")
else:
ut.addIfRequired(
result, sg, task.qf.evaluateFromDataset(task.data, sg),
task)
optimistic_estimate = task.qf.optimisticEstimateFromDataset(
task.data, sg) if isinstance(
task.qf,
m.BoundedInterestingnessMeasure) else float("inf")
# optimistic_estimate = task.qf.optimisticEstimateFromDataset(task.data, sg) if isinstance(task.qf, m.BoundedInterestingnessMeasure) else float("inf")
# quality = task.qf.evaluateFromDataset(task.data, sg)
# ut.addIfRequired (result, sg, quality, task)
if (optimistic_estimate >= ut.minimumRequiredQuality(
result, task)):
promising_candidates.append(
sg.subgroupDescription.selectors)
if (depth == task.depth):
break
# generate candidates next level
next_level_candidates = []
for i, sg1 in enumerate(promising_candidates):
for j, sg2 in enumerate(promising_candidates):
if (i < j and sg1[:-1] == sg2[:-1]):
candidate = list(sg1) + [sg2[-1]]
# check if ALL generalizations are contained in promising_candidates
generalization_descriptions = [[
x for x in candidate if x != sel
] for sel in candidate]
if all(g in promising_candidates
for g in generalization_descriptions):
next_level_candidates.append(
Subgroup(task.target, candidate))
depth = depth + 1
result.sort(key=lambda x: x[0], reverse=True)
return result