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 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 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 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 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)
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)
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)