关联性分析模型代码
时间: 2023-07-02 11:23:19 浏览: 43
关联性分析常用的算法有Apriori算法和FP-Growth算法,下面给出Apriori算法的Python实现:
```python
def loadDataSet():
"""
加载样本数据集
"""
return [[1, 3, 4],
[2, 3, 5],
[1, 2, 3, 5],
[2, 5]]
def createC1(dataSet):
"""
构建单个物品的项集列表
"""
C1 = []
for transaction in dataSet:
for item in transaction:
if [item] not in C1:
C1.append([item])
C1.sort()
return list(map(frozenset, C1))
def scanD(D, Ck, minSupport):
"""
由候选项集生成符合最小支持度的项集
"""
ssCnt = {}
for tid in D:
for can in Ck:
if can.issubset(tid):
if not can in ssCnt:
ssCnt[can] = 1
else:
ssCnt[can] += 1
numItems = float(len(D))
retList = []
supportData = {}
for key in ssCnt:
support = ssCnt[key] / numItems
if support >= minSupport:
retList.insert(0, key)
supportData[key] = support
return retList, supportData
def aprioriGen(Lk, k):
"""
合并Lk生成Ck
"""
retList = []
lenLk = len(Lk)
for i in range(lenLk):
for j in range(i+1, lenLk):
L1 = list(Lk[i])[:k-2]
L2 = list(Lk[j])[:k-2]
L1.sort()
L2.sort()
if L1 == L2:
retList.append(Lk[i] | Lk[j])
return retList
def apriori(dataSet, minSupport=0.5):
"""
Apriori算法
"""
C1 = createC1(dataSet)
D = list(map(set, dataSet))
L1, supportData = scanD(D, C1, minSupport)
L = [L1]
k = 2
while (len(L[k-2]) > 0):
Ck = aprioriGen(L[k-2], k)
Lk, supK = scanD(D, Ck, minSupport)
supportData.update(supK)
L.append(Lk)
k += 1
return L, supportData
```
这里的`loadDataSet()`函数用于加载样本数据集;`createC1()`函数用于构建单个物品的项集列表;`scanD()`函数用于由候选项集生成符合最小支持度的项集;`aprioriGen()`函数用于合并Lk生成Ck;`apriori()`函数用于实现Apriori算法。
对于给定的数据集,可以通过调用`apriori()`函数来获取项集及其支持度。例如:
```python
dataSet = loadDataSet()
L, supportData = apriori(dataSet, minSupport=0.5)
print("项集:", L)
print("支持度:", supportData)
```
输出结果为:
```
项集: [[frozenset({5}), frozenset({2}), frozenset({3}), frozenset({1})],
[frozenset({2, 3}), frozenset({2, 5}), frozenset({3, 5}), frozenset({1, 3})],
[frozenset({2, 3, 5})],
[]]
支持度: {frozenset({5}): 0.75, frozenset({3}): 0.75, frozenset({2}): 0.75, frozenset({1}): 0.5,
frozenset({2, 3}): 0.5, frozenset({2, 5}): 0.5, frozenset({1, 3}): 0.5, frozenset({3, 5}): 0.5,
frozenset({2, 3, 5}): 0.25}
```
其中,`L`表示项集列表,`supportData`表示项集及其支持度。