def execute(self, task):
result = []
queue = [(float("-inf"), ps.Conjunction([]))]
operator = ps.StaticSpecializationOperator(task.search_space)
task.qf.calculate_constant_statistics(task)
while queue:
q, old_description = heappop(queue)
q = -q
if not (q > ps.minimum_required_quality(result, task)):
break
for candidate_description in operator.refinements(old_description):
sg = candidate_description
statistics = task.qf.calculate_statistics(sg, task.data)
ps.add_if_required(result, sg,
task.qf.evaluate(sg, statistics), task)
optimistic_estimate = task.qf.optimistic_estimate(
sg, statistics)
# compute refinements and fill the queue
if len(
candidate_description
) < task.depth and optimistic_estimate >= ps.minimum_required_quality(
result, task):
heappush(queue,
(-optimistic_estimate, candidate_description))
result.sort(key=lambda x: x[0], reverse=True)
return ps.SubgroupDiscoveryResult(result, task)
def execute(self, task):
if not isinstance(task.qf, ps.StandardQFNumeric):
warnings.warn(
"BSD_numeric so far is only implemented for StandardQFNumeric")
self.pop_size = len(task.data)
sorted_data = task.data.sort_values(task.target.get_attributes(),
ascending=False)
# generate target bitset
self.target_values = sorted_data[task.target.get_attributes()
[0]].to_numpy()
task.qf.calculate_constant_statistics(task)
self.evaluate = task.qf.evaluate
# generate selector bitsets
self.bitsets = {}
for sel in task.search_space:
# generate bitset
self.bitsets[sel] = sel.covers(sorted_data)
result = self.search_internal(task, [], task.search_space, [],
np.ones(len(sorted_data), dtype=bool))
result.sort(key=lambda x: x[0], reverse=True)
return ps.SubgroupDiscoveryResult(result, task)
def execute(self, task):
task.qf.calculate_constant_statistics(task)
result = []
all_selectors = chain.from_iterable(
combinations(task.search_space, r)
for r in range(1, task.depth + 1))
if self.show_progress:
try:
from tqdm import tqdm
def binomial(x, y):
try:
binom = factorial(x) // factorial(y) // factorial(x -
y)
except ValueError:
binom = 0
return binom
total = sum(
binomial(len(task.search_space), k)
for k in range(1, task.depth + 1))
all_selectors = tqdm(all_selectors, total=total)
except ImportError:
pass
for selectors in all_selectors:
sg = ps.Conjunction(selectors)
statistics = task.qf.calculate_statistics(sg, task.data)
quality = task.qf.evaluate(sg, statistics)
ps.add_if_required(result, sg, quality, task)
result.sort(key=lambda x: x[0], reverse=True)
return ps.SubgroupDiscoveryResult(result, task)
def execute(self, task):
self.operator = ps.StaticSpecializationOperator(task.search_space)
task.qf.calculate_constant_statistics(task)
result = []
with self.apply_representation(task.data,
task.search_space) as representation:
self.search_internal(task, result, representation.Conjunction([]))
result.sort(key=lambda x: x[0], reverse=True)
return ps.SubgroupDiscoveryResult(result, task)
def execute(self, task):
result = []
queue = []
operator = ps.StaticGeneralizationOperator(task.search_space)
# init the first level
for sel in task.search_space:
queue.append((float("-inf"), ps.Disjunction([sel])))
task.qf.calculate_constant_statistics(task)
while queue:
q, candidate_description = heappop(queue)
q = -q
if q < ps.minimum_required_quality(result, task):
break
sg = candidate_description
statistics = task.qf.calculate_statistics(sg, task.data)
quality = task.qf.evaluate(sg, statistics)
ps.add_if_required(result,
sg,
quality,
task,
statistics=statistics)
qual = ps.minimum_required_quality(result, task)
if (quality, sg) in result:
new_queue = []
for q_tmp, c_tmp in queue:
if (-q_tmp) > qual:
heappush(new_queue, (q_tmp, c_tmp))
queue = new_queue
optimistic_estimate = task.qf.optimistic_estimate(sg, statistics)
# else:
# ps.add_if_required(result, sg, task.qf.evaluate_from_dataset(task.data, sg), task)
# optimistic_estimate = task.qf.optimistic_generalisation_from_dataset(task.data, sg) if qf_is_bounded else float("inf")
# compute refinements and fill the queue
if len(candidate_description) < task.depth and (
optimistic_estimate /
self.alpha**(len(candidate_description) + 1)
) >= ps.minimum_required_quality(result, task):
# print(qual)
# print(optimistic_estimate)
self.refined[len(candidate_description)] += 1
# print(str(candidate_description))
for new_description in operator.refinements(
candidate_description):
heappush(queue, (-optimistic_estimate, new_description))
else:
self.discarded[len(candidate_description)] += 1
result.sort(key=lambda x: x[0], reverse=True)
for qual, sg in result:
print("{} {}".format(qual, sg))
print("discarded " + str(self.discarded))
return ps.SubgroupDiscoveryResult(result, task)
def execute(self, task):
if not isinstance(task.qf, ps.BoundedInterestingnessMeasure):
raise RuntimeWarning(
"Quality function is unbounded, long runtime expected")
task.qf.calculate_constant_statistics(task)
with self.representation_type(task.data,
task.search_space) as representation:
combine_selectors = getattr(representation.__class__,
self.combination_name)
result = []
# init the first level
next_level_candidates = []
for sel in task.search_space:
next_level_candidates.append(combine_selectors([sel]))
# level-wise search
depth = 1
while next_level_candidates:
# check sgs from the last level
if self.use_vectorization:
promising_candidates = self.get_next_level_candidates_vectorized(
task, result, next_level_candidates)
else:
promising_candidates = self.get_next_level_candidates(
task, result, next_level_candidates)
if depth == task.depth:
break
if self.use_repruning:
promising_candidates = self.reprune_lower_levels(
promising_candidates, depth)
next_level_candidates_no_pruning = self.next_level(
promising_candidates)
# select those selectors and build a subgroup from them
# for which all subsets of length depth (=candidate length -1) are in the set of promising candidates
set_promising_candidates = set(
tuple(p) for p in promising_candidates)
next_level_candidates = [
combine_selectors(selectors)
for selectors in next_level_candidates_no_pruning
if all((subset in set_promising_candidates)
for subset in combinations(selectors, depth))
]
depth = depth + 1
result.sort(key=lambda x: x[0], reverse=True)
return ps.SubgroupDiscoveryResult(result, task)
def execute(self, task):
# adapt beam width to the result set size if desired
if self.beam_width_adaptive:
self.beam_width = task.result_set_size
# check if beam size is to small for result set
if self.beam_width < task.result_set_size:
raise RuntimeError(
'Beam width in the beam search algorithm is smaller than the result set size!'
)
task.qf.calculate_constant_statistics(task)
# init
beam = [(0, ps.Conjunction([]),
task.qf.calculate_statistics(slice(None), task.data))]
last_beam = None
depth = 0
while beam != last_beam and depth < task.depth:
last_beam = beam.copy()
for (_, last_sg, _) in last_beam:
if not getattr(last_sg, 'visited', False):
setattr(last_sg, 'visited', True)
for sel in task.search_space:
# create a clone
new_selectors = list(last_sg.selectors)
if sel not in new_selectors:
new_selectors.append(sel)
sg = ps.Conjunction(new_selectors)
statistics = task.qf.calculate_statistics(
sg, task.data)
quality = task.qf.evaluate(sg, statistics)
ps.add_if_required(beam,
sg,
quality,
task,
check_for_duplicates=True,
statistics=statistics)
depth += 1
# TODO make sure there is no bug here
result = beam[:task.result_set_size]
result.sort(key=lambda x: x[0], reverse=True)
return ps.SubgroupDiscoveryResult(result, task)
def execute(self, task):
result = []
queue = [(float("-inf"), ps.Conjunction([]))]
operator = SpecializationOperator(data=task.data.drop(['target'], axis=1), n_bins=self.n_bins,
max_features=self.max_features,
intervals_only=self.intervals_only,
binning=self.binning, specialization=self.specialization,
search_space=task.search_space)
task.qf.calculate_constant_statistics(task.data, task.target)
while queue:
q, old_description = heappop(queue)
q = -q
if not q > ps.minimum_required_quality(result, task):
break
for candidate_description in operator.refinements(old_description):
score_eval = task.qf.evaluate(candidate_description, task.target, task.data, None)
ps.add_if_required(result, candidate_description, score_eval, task)
if len(candidate_description) < task.depth:
heappush(queue, (-score_eval, candidate_description))
result.sort(key=lambda x: x[0], reverse=True)
return ps.SubgroupDiscoveryResult(result, task)
def execute(self, task, use_optimistic_estimates=True):
task.qf.calculate_constant_statistics(task)
result = self.search_internal(task, [], task.search_space, [],
use_optimistic_estimates)
result.sort(key=lambda x: x[0], reverse=True)
return ps.SubgroupDiscoveryResult(result, task)