def fit(self):
begin = datetime.now()
# Initialization
self._TermsManager = TermsManager(self._Dataset,
self.min_case_per_rule)
self._Pruner = Pruner(self._Dataset, self._TermsManager, self.alpha)
self._no_of_uncovered_cases = self._Dataset.get_no_of_uncovered_cases()
self._get_population_Survival()
while not self._global_stopping_condition():
# local variables
ant_index = 0
converg_test_index = 1
# Initialize rules
previous_rule = Rule(self._Dataset, self.alpha)
best_rule = copy.deepcopy(previous_rule)
# Local search
while not self._local_stopping_condition(ant_index,
converg_test_index):
current_rule = Rule(self._Dataset, self.alpha)
current_rule.construct(self._TermsManager,
self.min_case_per_rule)
current_rule = self._Pruner.prune(current_rule)
if current_rule.equals(previous_rule):
converg_test_index += 1
else:
converg_test_index = 1
if current_rule.fitness > best_rule.fitness:
best_rule = copy.deepcopy(current_rule)
self._TermsManager.pheromone_updating(current_rule.antecedent,
current_rule.fitness)
previous_rule = copy.deepcopy(current_rule)
ant_index += 1
# case: local search didnt find any exceptional rules
if best_rule.fitness < 1 - self.alpha:
break
# saving local search results
elif self._can_add_rule(
best_rule): # check if rule already exists on the list
self.discovered_rule_list.append(best_rule)
self._Dataset.update_covered_cases(best_rule.sub_group_cases)
self._no_of_uncovered_cases = self._Dataset.get_no_of_uncovered_cases(
)
self._TermsManager.pheromone_init()
self._iterations += 1
self._run_time = datetime.now() - begin
# generates the rules representative strings
for index, rule in enumerate(self.discovered_rule_list):
rule.set_string_repr(index)
rule.set_KMmodel()
return
def fit(self):
# Initialization
self._TermsManager = TermsManager(self._Dataset,
self.min_case_per_rule)
self._Pruner = Pruner(self._Dataset, self._TermsManager)
self._no_of_uncovered_cases = self._Dataset.get_no_of_uncovered_cases()
converg_list_index = 0
while not self._global_stopping_condition(converg_list_index):
# local variables
ant_index = 0
converg_test_index = 1
# Initialize rules
previous_rule = Rule(self._Dataset)
best_rule = copy.deepcopy(previous_rule)
best_rule.quality = 1 - UserInputs.alpha
while not self._local_stopping_condition(ant_index,
converg_test_index):
current_rule = Rule(self._Dataset)
current_rule.construct(self._TermsManager,
self.min_case_per_rule)
current_rule = self._Pruner.prune(current_rule)
if current_rule.equals(previous_rule):
converg_test_index += 1
else:
converg_test_index = 1
if current_rule.quality > best_rule.quality:
best_rule = copy.deepcopy(current_rule)
self._TermsManager.pheromone_updating(current_rule.antecedent,
current_rule.quality)
previous_rule = copy.deepcopy(current_rule)
ant_index += 1
if best_rule.quality == 1 - UserInputs.alpha: # did not generate any rules
break
else:
if self._can_add_rule(
best_rule): # check if rule already exists on the list
self.discovered_rule_list.append(best_rule)
self._Dataset.update_covered_cases(
best_rule.sub_group_cases)
self._no_of_uncovered_cases = self._Dataset.get_no_of_uncovered_cases(
)
converg_list_index = 0
else:
converg_list_index += 1
self._TermsManager.pheromone_init()
self._iterations += 1
# END OF WHILE (AVAILABLE_CASES > MAX_UNCOVERED_CASES)
# generates the rules representative strings
for index, rule in enumerate(self.discovered_rule_list):
rule.set_string_repr(index)
return
