def getMaxGeneralizationTargetShare(data, subgroup, weightingAttribute=None):
selectors = subgroup.subgroupDescription.selectors
generalizations = ut.powerset(selectors)
maxTargetShare = 0
for sels in generalizations:
sgd = SubgroupDescription(list(sels))
sg = Subgroup(subgroup.target, sgd)
(_, _, instancesSubgroup,
positivesSubgroup) = sg.get_base_statistics(data, weightingAttribute)
targetShare = positivesSubgroup / instancesSubgroup
maxTargetShare = max(maxTargetShare, targetShare)
return maxTargetShare
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