fpgrowth算法python代码

FP-growth算法是一种用于频繁模式挖掘的快速和高效的算法。下面是FP-growth算法的Python代码：

# 定义FP树的节点类
class TreeNode:
    def __init__(self, name, count, parent):
        self.name = name # 节点名称
        self.count = count # 节点计数
        self.parent = parent # 父节点
        self.children = {} # 子节点

    # 增加节点计数
    def increment(self, count):
        self.count += count

# 构建FP树
def createFPtree(dataset, minSupport):
    headerTable = {}
    # 第一次遍历数据集，创建头指针表
    for trans in dataset:
        for item in trans:
            headerTable[item] = headerTable.get(item, 0) + dataset[trans]

    # 移除不满足最小支持度的项
    headerTable = {k: v for k, v in headerTable.items() if v >= minSupport}
    freqItemSet = set(headerTable.keys())
    if len(freqItemSet) == 0: # 如果没有项满足最小支持度，则退出
        return None, None

    # 更新头指针表
    for k in headerTable:
        headerTable[k] = [headerTable[k], None]

    # 创建根节点
    root = TreeNode('NULL', 1, None)

    # 第二次遍历数据集，构建FP树
    for trans, count in dataset.items():
        localD = {}
        for item in trans:
            if item in freqItemSet:
                localD[item] = headerTable[item][0]

        if len(localD) > 0:
            orderedItems = [v[0] for v in sorted(localD.items(), key=lambda p: p[1], reverse=True)]
            updateFPtree(orderedItems, root, headerTable, count)

    return root, headerTable

# 更新FP树
def updateFPtree(items, root, headerTable, count):
    if items[0] in root.children:
        root.children[items[0]].increment(count)
    else:
        root.children[items[0]] = TreeNode(items[0], count, root)

        if headerTable[items[0]][1] is None:
            headerTable[items[0]][1] = root.children[items[0]]
        else:
            updateHeader(headerTable[items[0]][1], root.children[items[0]])

    if len(items) > 1:
        updateFPtree(items[1:], root.children[items[0]], headerTable, count)

# 更新头指针表
def updateHeader(nodeToTest, targetNode):
    while nodeToTest.nodeLink is not None:
        nodeToTest = nodeToTest.nodeLink
    nodeToTest.nodeLink = targetNode

# 抽取条件模式基
def findPrefixPath(treeNode):
    conditionPatterns = {}
    while treeNode is not None:
        prefixPath = []
        ascendTree(treeNode, prefixPath)
        if len(prefixPath) > 1:
            conditionPatterns[frozenset(prefixPath[1:])] = treeNode.count
        treeNode = treeNode.nodeLink
    return conditionPatterns

# 递归上溯FP树
def ascendTree(treeNode, prefixPath):
    if treeNode.parent is not None:
        prefixPath.append(treeNode.name)
        ascendTree(treeNode.parent, prefixPath)

# 递归查找频繁项集
def mineFPtree(headerTable, minSupport, preFix, freqItemList):
    sortedItemList = [v[0] for v in sorted(headerTable.items(), key=lambda p: p[1][0])]
    for basePat in sortedItemList:
        newFreqSet = preFix.copy()
        newFreqSet.add(basePat)
        freqItemList.append(newFreqSet)
        conditionPatterns = findPrefixPath(headerTable[basePat][1])
        myCondTree, myHead = createFPtree(conditionPatterns, minSupport)

        if myHead is not None:
            mineFPtree(myHead, minSupport, newFreqSet, freqItemList)

# FP-growth算法
def fpGrowth(dataset, minSupport):
    headerTable = {}
    # 第一次遍历数据集，创建头指针表
    for trans in dataset:
        for item in trans:
            headerTable[item] = headerTable.get(item, 0) + dataset[trans]

    # 移除不满足最小支持度的项
    headerTable = {k: v for k, v in headerTable.items() if v >= minSupport}
    freqItemSet = set(headerTable.keys())
    if len(freqItemSet) == 0: # 如果没有项满足最小支持度，则退出
        return []

    for k in headerTable:
        headerTable[k] = [headerTable[k], None]

    freqItemList = []
    mineFPtree(headerTable, minSupport, set(), freqItemList)
    
    return freqItemList

# 使用示例
dataset = [['A', 'B', 'C', 'D'],
           ['B', 'D'],
           ['A', 'B', 'E'],
           ['A', 'B', 'C', 'D', 'E'],
           ['A', 'D', 'E'],
           ['A', 'B', 'C'],
           ['A', 'C', 'D', 'E'],
           ['B', 'C', 'E']]
minSupport = 2

freqItemSet = fpGrowth(dataset, minSupport)
print(freqItemSet)

以上是FP-growth算法的Python代码，通过构建FP树和递归查找频繁项集的方式进行频繁模式挖掘。