def generate_negative_rules(self, train, confident_value_pass, zone_confident_pass):
class_value_arr = self.get_class_value_array(train)
self.prepare_data_rows(train)
for i in range(0, len(self.rule_base_array)):
rule_negative = Rule(self.data_base)
rule_negative.antecedent = self.rule_base_array[i].antecedent
positive_rule_class_value = self.rule_base_array[i].get_class()
print("the positive rule class value is " + str(positive_rule_class_value) + " ,the i is :" + str(i))
# rule_negative.setClass(positive_rule_class_value)
for j in range(0, len(class_value_arr)):
class_type = int(class_value_arr[j])
if positive_rule_class_value != class_type: # need to get another class value for negative rule
rule_negative.setClass(class_type) # change the class type in the rule
rule_negative.calculate_confident_support(self.data_row_array)
print("Negative rule's confident value is :" + str(rule_negative.confident_value))
if rule_negative.confident_value > confident_value_pass and rule_negative.zone_confident > zone_confident_pass:
rule_negative.weight = rule_negative.confident_value
if not (self.duplicated_negative_rule(rule_negative)):
for k in range(0, len(rule_negative.antecedent)):
print("antecedent L_ " + str(rule_negative.antecedent[j]))
# print("Negative rule's class value " + str(rule_negative.get_class()))
# print(" Negative rule's weight, confident_vale " + str(rule_negative.weight))
# print(" Negative rule's zone confident value " + str(rule_negative.zone_confident))
# print("Negative rule's positive_rule_class_value" + str(positive_rule_class_value))
# print("Negative rule's class_type" + str(class_type))
self.negative_rule_base_array.append(rule_negative)
def get_Rule(self, slist, bookmark, weight):
def logged_match(m, file, variables):
common.debug("Testing %s against %s rule (%s)" % (file, self.match_token[0], " ".join(slist)))
return m(file, variables)
r = Rule()
match = self.get_match_function(slist, {"bookmark": bookmark, "weight": weight})
if match is not None:
r.bookmark = bookmark
r.weight = weight
r.text = " ".join(slist)
r.match_func = lambda file, variables: logged_match(match, file, variables)
r.match_token = self.match_token[0]
return r
def generate_negative_rules(self, train, confident_value_pass):
confident_value = 0
class_value_arr = self.get_class_value_array(train)
for i in range(0, len(self.ruleBase)):
rule_negative = Rule()
rule_negative.antecedent = self.ruleBase[i].antecedent
positive_rule_class_value = self.ruleBase[i].get_class()
print("the positive rule class value is " +
str(positive_rule_class_value) + " ,the i is :" + str(i))
rule_negative.setClass(positive_rule_class_value)
for j in range(0, len(class_value_arr)):
class_type = int(class_value_arr[j])
if positive_rule_class_value != class_type: # need to get another class value for negative rule
rule_negative.setClass(
class_type) # change the class type in the rule
confident_value = rule_negative.calculate_confident(
self.data_row_array)
print("The calculation confident value is :" +
str(confident_value))
if confident_value >= confident_value_pass:
rule_negative.weight = confident_value
if not (self.duplicated_negative_rule(rule_negative)):
for k in range(0, len(rule_negative.antecedent)):
print("antecedent L_ " +
str(rule_negative.antecedent[j].label))
print("class value " +
str(rule_negative.get_class()))
print(" weight " + str(rule_negative.weight))
print("positive_rule_class_value" +
str(positive_rule_class_value))
print("class_type" + str(class_type))
self.negative_rule_base_array.append(rule_negative)
