def strNumClassRules(rules):
list_string = []
for consequent in mlem2.getEstimatedClass(rules):
rules_consequent = mlem2.getRulesClass(rules, consequent)
num = len(rules_consequent)
list_string.append(str(num))
return ",".join(list_string)
def strNumClassRules(rules):
list_string = []
for consequent in mlem2.getEstimatedClass(rules):
rules_consequent = mlem2.getRulesClass(rules, consequent)
num = len(rules_consequent)
list_string.append(str(num))
return (",".join(list_string))
def getRuleClusteringBySimilarity(rules, colnames, list_judgeNominal, k=3) :
rules_new = list()
# 結論部別
for cls in mlem2.getEstimatedClass(rules) :
target_rules = [r for r in rules if r.getConsequent() == cls]
# ルール群のサポート値の最小値がk以下のルールがある内は繰り返す
min_support = mlem2.getMinSupport(target_rules)
while min_support < k :
# target_rules が 1つなら
if len(target_rules) == 1 :
target_rules.pop()
break
# merge対象ルールを見つける
merged_rules = [r for r in target_rules if len(r.getSupport()) == min_support]
merged_rule = merged_rules[0]
target_rules.remove(merged_rule)
# merged_rule との類似度を求める
list_similarities = [getSimilarity2(merged_rule, r, colnames, list_judgeNominal) for r in target_rules]
# 最も類似度が大きいルールを見つける
max_similarity = np.max(list_similarities)
max_rules = [target_rules[i] for i,s in enumerate(list_similarities) if s == max_similarity]
#print("First : " + str(len(max_rules)))
# 一意でなければ、条件部を構成する属性数で判断
if len(max_rules) > 1 :
list_count_same_conditions = [getCountSameCondition(merged_rule, r) for r in max_rules]
max_count = np.max(list_count_same_conditions)
max_rules = [max_rules[i] for i,c in enumerate(list_count_same_conditions) if c == max_count]
#print("Second : " + str(len(max_rules)))
# 一意でなければ、supportの小ささで判断
if len(max_rules) > 1 :
list_supports = [len(r.getSupport()) for r in max_rules]
min_support = np.min(list_supports)
max_rules = [max_rules[i] for i,s in enumerate(list_supports) if s == min_support]
#print("Third : " + str(len(max_rules)))
# 先頭のルールでmerge
merge_rule = mergeRule(merged_rule, max_rules[0])
target_rules.remove(max_rules[0])
# 新しいルールを追加
target_rules.append(merge_rule)
# min_support 更新
min_support = mlem2.getMinSupport(target_rules)
print(min_support)
rules_new.extend(target_rules)
return(rules_new)
def getMRulesFUN(list_rules, attr, v, target_cls, DELFUN, m = 0) :
num_target_cls, num_other_cls, list_num_other_cls = 0, 0, []
classes = mlem2.getEstimatedClass(list_rules)
for cls in classes :
if cls == target_cls :
num_target_cls = getNumRulesClassIncludeE(list_rules, attr, v, cls)
else :
list_num_other_cls.append(getNumRulesClassIncludeE(list_rules, attr, v, cls))
num_other_cls = sum(list_num_other_cls) / len(list_num_other_cls) #複数クラスの場合を考慮
if (num_target_cls / (num_target_cls + num_other_cls)) > m : #m保護なら
return(list_rules)
else :
return(DELFUN(list_rules, attr, v))
def getMRulesFUN(list_rules, attr, v, target_cls, DELFUN, m = 0) :
num_target_cls, num_other_cls, list_num_other_cls = 0, 0, []
classes = mlem2.getEstimatedClass(list_rules)
for cls in classes :
if cls == target_cls :
num_target_cls = getNumRulesClassIncludeE(list_rules, attr, v, cls)
else :
list_num_other_cls.append(getNumRulesClassIncludeE(list_rules, attr, v, cls))
num_other_cls = sum(list_num_other_cls) / len(list_num_other_cls) #複数クラスの場合を考慮
if (num_target_cls / (num_target_cls + num_other_cls)) > m : #m保護なら
return(list_rules)
else :
return(DELFUN(list_rules, attr, v))
def getRuleClusteringBySameCondition(rules, k=3):
rules_new = list()
# 結論部別
for cls in mlem2.getEstimatedClass(rules):
target_rules = [r for r in rules if r.getConsequent() == cls]
# ルール群のサポート値の最小値がk以下のルールがある内は繰り返す
min_support = mlem2.getMinSupport(target_rules)
while min_support < k:
# target_rules が 1つなら
if len(target_rules) == 1:
target_rules.pop()
break
# merge対象ルールを見つける
merged_rules = [
r for r in target_rules if len(r.getSupport()) == min_support
]
merged_rule = merged_rules[0]
target_rules.remove(merged_rule)
# 同じ条件属性の数でマージするルールを決定
list_count_same_conditions = [
getCountSameCondition(merged_rule, r) for r in target_rules
]
max_count = np.max(list_count_same_conditions)
max_rules = [
target_rules[i]
for i, c in enumerate(list_count_same_conditions)
if c == max_count
]
# 先頭のルールでmerge
merge_rule = mergeRule(merged_rule, max_rules[0])
target_rules.remove(max_rules[0])
# 新しいルールを追加
target_rules.append(merge_rule)
# min_support 更新
min_support = mlem2.getMinSupport(target_rules)
print(min_support)
rules_new.extend(target_rules)
return (rules_new)
def getAccurayRecall(rules, decision_table, list_judgeNominal):
result = []
consequents = mlem2.getEstimatedClass(rules)
for consequent in consequents :
target_rules = mlem2.getRulesClass(rules, consequent)
target_objects = []
for rule in target_rules :
match_objects = decision_table.apply(lambda obj: isExplainRule(obj, rule, list_judgeNominal), axis=1)
index_objects = decision_table[match_objects].index.tolist()
target_objects.extend(index_objects)
target_objects = list(set(target_objects))
estimated_classes = decision_table[decision_table.columns[-1]].ix[target_objects]
accuracy = sum(estimated_classes == consequent)/ len(target_objects)
recall = sum(estimated_classes == consequent) / sum(decision_table[decision_table.columns[-1]] == consequent)
result.append((accuracy, recall))
return(result)
def getAccurayRecall(rules, decision_table, list_judgeNominal):
result = []
consequents = mlem2.getEstimatedClass(rules)
for consequent in consequents:
target_rules = mlem2.getRulesClass(rules, consequent)
target_objects = []
for rule in target_rules:
match_objects = decision_table.apply(
lambda obj: isExplainRule(obj, rule, list_judgeNominal),
axis=1)
index_objects = decision_table[match_objects].index.tolist()
target_objects.extend(index_objects)
target_objects = list(set(target_objects))
estimated_classes = decision_table[
decision_table.columns[-1]].ix[target_objects]
accuracy = sum(estimated_classes == consequent) / len(target_objects)
recall = sum(estimated_classes == consequent) / sum(
decision_table[decision_table.columns[-1]] == consequent)
result.append((accuracy, recall))
return (result)
def getRuleClusteringByRandom(rules, k=3):
rules_new = list()
# 結論部別
for cls in mlem2.getEstimatedClass(rules):
target_rules = [r for r in rules if r.getConsequent() == cls]
# ルール群のサポート値の最小値がk以下のルールがある内は繰り返す
min_support = mlem2.getMinSupport(target_rules)
while min_support < k:
# target_rules が 1つなら
if len(target_rules) == 1:
target_rules.pop()
break
# merge対象ルールを見つける
merged_rules = [
r for r in target_rules if len(r.getSupport()) == min_support
]
merged_rule = merged_rules[0]
target_rules.remove(merged_rule)
# ランダムにもう一つのルールを求める
random_rule = choiceRandomRule(target_rules)
# 先頭のルールでmerge
merge_rule = mergeRule(merged_rule, random_rule)
target_rules.remove(random_rule)
# 新しいルールを追加
target_rules.append(merge_rule)
# min_support 更新
min_support = mlem2.getMinSupport(target_rules)
print(min_support)
rules_new.extend(target_rules)
return (rules_new)
def getRuleClusteringBySameCondition(rules, k=3) :
rules_new = list()
# 結論部別
for cls in mlem2.getEstimatedClass(rules) :
target_rules = [r for r in rules if r.getConsequent() == cls]
# ルール群のサポート値の最小値がk以下のルールがある内は繰り返す
min_support = mlem2.getMinSupport(target_rules)
while min_support < k :
# target_rules が 1つなら
if len(target_rules) == 1 :
target_rules.pop()
break
# merge対象ルールを見つける
merged_rules = [r for r in target_rules if len(r.getSupport()) == min_support]
merged_rule = merged_rules[0]
target_rules.remove(merged_rule)
# 同じ条件属性の数でマージするルールを決定
list_count_same_conditions = [getCountSameCondition(merged_rule, r) for r in target_rules]
max_count = np.max(list_count_same_conditions)
max_rules = [target_rules[i] for i,c in enumerate(list_count_same_conditions) if c == max_count]
# 先頭のルールでmerge
merge_rule = mergeRule(merged_rule, max_rules[0])
target_rules.remove(max_rules[0])
# 新しいルールを追加
target_rules.append(merge_rule)
# min_support 更新
min_support = mlem2.getMinSupport(target_rules)
print(min_support)
rules_new.extend(target_rules)
return(rules_new)
def getRuleClusteringByRandom(rules, k=3) :
rules_new = list()
# 結論部別
for cls in mlem2.getEstimatedClass(rules) :
target_rules = [r for r in rules if r.getConsequent() == cls]
# ルール群のサポート値の最小値がk以下のルールがある内は繰り返す
min_support = mlem2.getMinSupport(target_rules)
while min_support < k :
# target_rules が 1つなら
if len(target_rules) == 1 :
target_rules.pop()
break
# merge対象ルールを見つける
merged_rules = [r for r in target_rules if len(r.getSupport()) == min_support]
merged_rule = merged_rules[0]
target_rules.remove(merged_rule)
# ランダムにもう一つのルールを求める
random_rule = choiceRandomRule(target_rules)
# 先頭のルールでmerge
merge_rule = mergeRule(merged_rule, random_rule)
target_rules.remove(random_rule)
# 新しいルールを追加
target_rules.append(merge_rule)
# min_support 更新
min_support = mlem2.getMinSupport(target_rules)
print(min_support)
rules_new.extend(target_rules)
return(rules_new)
def getRuleClusteringBySimilarity(rules, colnames, list_judgeNominal, k=3):
rules_new = list()
# 結論部別
for cls in mlem2.getEstimatedClass(rules):
target_rules = [r for r in rules if r.getConsequent() == cls]
# ルール群のサポート値の最小値がk以下のルールがある内は繰り返す
min_support = mlem2.getMinSupport(target_rules)
while min_support < k:
# target_rules が 1つなら
if len(target_rules) == 1:
target_rules.pop()
break
# merge対象ルールを見つける
merged_rules = [
r for r in target_rules if len(r.getSupport()) == min_support
]
merged_rule = merged_rules[0]
target_rules.remove(merged_rule)
# merged_rule との類似度を求める
list_similarities = [
getSimilarity2(merged_rule, r, colnames, list_judgeNominal)
for r in target_rules
]
# 最も類似度が大きいルールを見つける
max_similarity = np.max(list_similarities)
max_rules = [
target_rules[i] for i, s in enumerate(list_similarities)
if s == max_similarity
]
#print("First : " + str(len(max_rules)))
# 一意でなければ、条件部を構成する属性数で判断
if len(max_rules) > 1:
list_count_same_conditions = [
getCountSameCondition(merged_rule, r) for r in max_rules
]
max_count = np.max(list_count_same_conditions)
max_rules = [
max_rules[i]
for i, c in enumerate(list_count_same_conditions)
if c == max_count
]
#print("Second : " + str(len(max_rules)))
# 一意でなければ、supportの小ささで判断
if len(max_rules) > 1:
list_supports = [len(r.getSupport()) for r in max_rules]
min_support = np.min(list_supports)
max_rules = [
max_rules[i] for i, s in enumerate(list_supports)
if s == min_support
]
#print("Third : " + str(len(max_rules)))
# 先頭のルールでmerge
merge_rule = mergeRule(merged_rule, max_rules[0])
target_rules.remove(max_rules[0])
# 新しいルールを追加
target_rules.append(merge_rule)
# min_support 更新
min_support = mlem2.getMinSupport(target_rules)
print(min_support)
rules_new.extend(target_rules)
return (rules_new)
def getRuleClusteringByConsistentExceptMRule(rules, colnames, list_judgeNominal, k=3, m=3) :
rules_new = list()
# 結論部別
for cls in mlem2.getEstimatedClass(rules) :
cls_rules = [r for r in rules if r.getConsequent() == cls]
# m未満のルールとm以上のルールに分ける
target_rules = [r for r in cls_rules if len(r.getSupport()) < m]
no_target_rules = [r for r in cls_rules if r not in target_rules]
# ルール群のサポート値の最小値がk以下のルールがある内は繰り返す
min_support = mlem2.getMinSupport(target_rules) if target_rules else 0
while min_support < k and target_rules:
# target_rules が 1つならそれを削除して繰り返しを抜ける
if len(target_rules) == 1 :
target_rules.pop()
break
# merge対象ルールを見つける
merged_rules = [r for r in target_rules if len(r.getSupport()) == min_support]
merged_rule = merged_rules[0]
target_rules.remove(merged_rule)
# 斜め発生度合いを計算
list_inconsistency = [getElementDiscernibieRule(r, merged_rule) for r in target_rules]
# もっとも斜めが発生しないルールに絞る
max_inconsistency = np.max(list_inconsistency)
max_rules = [target_rules[i] for i,c in enumerate(list_inconsistency) if c == max_inconsistency]
# 一意でなければ、条件部を構成する属性数で判断
if len(max_rules) > 1 :
list_count_same_conditions = [getCountSameCondition(merged_rule, r) for r in max_rules]
max_count = np.max(list_count_same_conditions)
max_rules = [max_rules[i] for i,c in enumerate(list_count_same_conditions) if c == max_count]
#print("Second : " + str(len(max_rules)))
# 一意でなければ、supportの小ささで判断
if len(max_rules) > 1 :
list_supports = [len(r.getSupport()) for r in max_rules]
min_support = np.min(list_supports)
max_rules = [max_rules[i] for i,s in enumerate(list_supports) if s == min_support]
#print("Third : " + str(len(max_rules)))
# 先頭のルールでmerge
merge_rule = mergeRule(merged_rule, max_rules[0])
target_rules.remove(max_rules[0])
# 新しいルールを追加
target_rules.append(merge_rule)
# min_support 更新
min_support = mlem2.getMinSupport(target_rules)
print(min_support)
rules_new.extend(target_rules)
rules_new.extend(no_target_rules)
return(rules_new)
accuracy_score(decision_class, predictions)
# rules の数を求める
num = len(rules)
# 平均の長さを求める
mean_length = mlem2.getMeanLength(rules)
# train data setup
decision_table_train, decision_class = getData(FILENAME, iter1, iter2, T = "train")
list_judgeNominal = getJudgeNominal(decision_table_train, FILENAME)
# 平均支持度と平均確信度を求める
mean_support, mean_conf = LERS.getSupportConfidenceRules(rules, decision_table_train, list_judgeNominal)
# AccとRecallを求める
acc_recall = LERS.getAccurayRecall(rules, decision_table_train, list_judgeNominal)
for i,c in enumerate(mlem2.getEstimatedClass(rules)):
print(str(acc_recall[i][0])+","+str(acc_recall[i][1]))
###### 公正配慮のテスト
# 基本条件を含むルールセット
rules_sex_2 = mlem2.getRulesIncludeE(rules, "Sex_Marital_Status", "2.0")
rules_sex_4 = mlem2.getRulesIncludeE(rules, "Sex_Marital_Status", "4.0")
# 条件を含まないルールセット
rules_exclude_sex = mlem2.getRulesExcludeAttr(rules, "Sex_Marital_Status")
# 基本条件を含まないルールセット
rules_exclude_sex_1 = mlem2.getRulesExcludeE(rules, "Sex_Marital_Status", "1.0")
# 条件を削除したルールセット
rules_del_value = mlem2.getRulesDelAttr(rules, "Value_Savings_Stocks")
# 基本条件を削除したルールセット
rules_del_value_1 = mlem2.getRulesDelE(rules, "Value_Savings_Stocks", "1.0")
def strAccRecall(rules, acc_recall):
list_string = []
for i, c in enumerate(mlem2.getEstimatedClass(rules)):
list_string.append(str(acc_recall[i][0]) + "," + str(acc_recall[i][1]))
return ",".join(list_string)
accuracy_score(decision_class, predictions)
# rules の数を求める
num = len(rules)
# 平均の長さを求める
mean_length = mlem2.getMeanLength(rules)
# train data setup
decision_table_train, decision_class = getData(FILENAME, iter1, iter2, T = "train")
list_judgeNominal = getJudgeNominal(decision_table_train, FILENAME)
# 平均支持度と平均確信度を求める
mean_support, mean_conf = LERS.getSupportConfidenceRules(rules, decision_table_train, list_judgeNominal)
# AccとRecallを求める
acc_recall = LERS.getAccurayRecall(rules, decision_table_train, list_judgeNominal)
for i,c in enumerate(mlem2.getEstimatedClass(rules)):
print(str(acc_recall[i][0])+","+str(acc_recall[i][1]))
###### 公正配慮のテスト
# 基本条件を含むルールセット
rules_sex_2 = mlem2.getRulesIncludeE(rules, "Sex_Marital_Status", "2.0")
rules_sex_4 = mlem2.getRulesIncludeE(rules, "Sex_Marital_Status", "4.0")
# 条件を含まないルールセット
rules_exclude_sex = mlem2.getRulesExcludeAttr(rules, "Sex_Marital_Status")
# 基本条件を含まないルールセット
rules_exclude_sex_1 = mlem2.getRulesExcludeE(rules, "Sex_Marital_Status", "1.0")
# 条件を削除したルールセット
rules_del_value = mlem2.getRulesDelAttr(rules, "Value_Savings_Stocks")
# 基本条件を削除したルールセット
rules_del_value_1 = mlem2.getRulesDelE(rules, "Value_Savings_Stocks", "1.0")
def strAccRecall(rules, acc_recall):
list_string = []
for i, c in enumerate(mlem2.getEstimatedClass(rules)):
list_string.append(str(acc_recall[i][0]) + "," + str(acc_recall[i][1]))
return (",".join(list_string))
def getRuleClusteringByConsistentExceptMRule(rules,
colnames,
list_judgeNominal,
k=3,
m=3):
rules_new = list()
# 結論部別
for cls in mlem2.getEstimatedClass(rules):
cls_rules = [r for r in rules if r.getConsequent() == cls]
# m未満のルールとm以上のルールに分ける
target_rules = [r for r in cls_rules if len(r.getSupport()) < m]
no_target_rules = [r for r in cls_rules if r not in target_rules]
# ルール群のサポート値の最小値がk以下のルールがある内は繰り返す
min_support = mlem2.getMinSupport(target_rules) if target_rules else 0
while min_support < k and target_rules:
# target_rules が 1つならそれを削除して繰り返しを抜ける
if len(target_rules) == 1:
target_rules.pop()
break
# merge対象ルールを見つける
merged_rules = [
r for r in target_rules if len(r.getSupport()) == min_support
]
merged_rule = merged_rules[0]
target_rules.remove(merged_rule)
# 斜め発生度合いを計算
list_inconsistency = [
getElementDiscernibieRule(r, merged_rule) for r in target_rules
]
# もっとも斜めが発生しないルールに絞る
max_inconsistency = np.max(list_inconsistency)
max_rules = [
target_rules[i] for i, c in enumerate(list_inconsistency)
if c == max_inconsistency
]
# 一意でなければ、条件部を構成する属性数で判断
if len(max_rules) > 1:
list_count_same_conditions = [
getCountSameCondition(merged_rule, r) for r in max_rules
]
max_count = np.max(list_count_same_conditions)
max_rules = [
max_rules[i]
for i, c in enumerate(list_count_same_conditions)
if c == max_count
]
#print("Second : " + str(len(max_rules)))
# 一意でなければ、supportの小ささで判断
if len(max_rules) > 1:
list_supports = [len(r.getSupport()) for r in max_rules]
min_support = np.min(list_supports)
max_rules = [
max_rules[i] for i, s in enumerate(list_supports)
if s == min_support
]
#print("Third : " + str(len(max_rules)))
# 先頭のルールでmerge
merge_rule = mergeRule(merged_rule, max_rules[0])
target_rules.remove(max_rules[0])
# 新しいルールを追加
target_rules.append(merge_rule)
# min_support 更新
min_support = mlem2.getMinSupport(target_rules)
print(min_support)
rules_new.extend(target_rules)
rules_new.extend(no_target_rules)
return (rules_new)
