fpgrowth算法python

FP-growth算法是一种用于发现频繁项集的数据挖掘算法，它基于一种称为FP树的数据结构。FP-growth算法的优点是它只需要对数据集进行两次扫描，相对于其他频繁项集挖掘算法来说效率更高。

在Python中，可以使用fp-growth库来实现FP-growth算法。在该库的实现中，首先需要将数据集转化为一种特定的格式，然后调用fp-growth函数进行频繁项集的挖掘。具体使用方法可以参考蕉叉熵博客中的文章，其中提供了关于FP-growth算法的Python实现示例代码。

然而，需要注意的是，在博客中的示例代码中可能存在一些问题，比如在排列频繁项时使用列表推导可能会出现错误。因此，你在实际使用中可能需要注意并修复代码中的潜在问题。

另外，你还可以借助其他开源库，如pyfpgrowth，它提供了对FP-growth算法的实现。在使用pyfpgrowth库时，你需要将数据集转化为所需的格式，并设置最小支持度计数。然后，调用find_frequent_patterns函数即可获得频繁项集。

相关问题

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。

FpGrowth算法python实现

FP-Growth算法是一种用于挖掘频繁模式的算法，它基于一种称为FP树的数据结构，能够在挖掘频繁项集时避免生成大量的候选项集。下面是FP-Growth算法的Python实现。

首先，我们需要定义一个类来表示FP树的节点：

class TreeNode:
    def __init__(self, name, count, parent):
        self.name = name
        self.count = count
        self.parent = parent
        self.children = {}
        self.nodeLink = None

    def inc(self, count):
        self.count += count

    def display(self, ind=1):
        print(' ' * ind, self.name, ' ', self.count)
        for child in self.children.values():
            child.display(ind + 1)

然后，我们需要定义函数来构建FP树：

def createTree(dataSet, minSup=1):
    headerTable = {}
    for trans in dataSet:
        for item in trans:
            headerTable[item] = headerTable.get(item, 0) + dataSet[trans]

    for k in list(headerTable):
        if headerTable[k] < minSup:
            del(headerTable[k])

    freqItemSet = set(headerTable.keys())
    if len(freqItemSet) == 0:
        return None, None

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

    retTree = TreeNode('Null Set', 1, None)
    for tranSet, count in dataSet.items():
        localD = {}
        for item in tranSet:
            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)]
            updateTree(orderedItems, retTree, headerTable, count)

    return retTree, headerTable

接下来，我们需要定义函数来更新FP树：

def updateTree(items, inTree, headerTable, count):
    if items[0] in inTree.children:
        inTree.children[items[0]].inc(count)
    else:
        inTree.children[items[0]] = TreeNode(items[0], count, inTree)
        if headerTable[items[0]][1] == None:
            headerTable[items[0]][1] = inTree.children[items[0]]
        else:
            updateHeader(headerTable[items[0]][1], inTree.children[items[0]])
    if len(items) > 1:
        updateTree(items[1:], inTree.children[items[0]], headerTable, count)

def updateHeader(nodeToTest, targetNode):
    while (nodeToTest.nodeLink != None):
        nodeToTest = nodeToTest.nodeLink
    nodeToTest.nodeLink = targetNode

最后，我们需要定义函数来挖掘频繁模式：

def ascendTree(leafNode, prefixPath):
    if leafNode.parent != None:
        prefixPath.append(leafNode.name)
        ascendTree(leafNode.parent, prefixPath)

def findPrefixPath(basePat, treeNode):
    condPats = {}
    while treeNode != None:
        prefixPath = []
        ascendTree(treeNode, prefixPath)
        if len(prefixPath) > 1:
            condPats[frozenset(prefixPath[1:])] = treeNode.count
        treeNode = treeNode.nodeLink
    return condPats

def mineTree(inTree, headerTable, minSup, preFix, freqItemList):
    bigL = [v[0] for v in sorted(headerTable.items(), key=lambda p: p[1])]
    for basePat in bigL:
        newFreqSet = preFix.copy()
        newFreqSet.add(basePat)
        freqItemList.append(newFreqSet)
        condPattBases = findPrefixPath(basePat, headerTable[basePat][1])
        myCondTree, myHead = createTree(condPattBases, minSup)
        if myHead != None:
            mineTree(myCondTree, myHead, minSup, newFreqSet, freqItemList)

使用FP-Growth算法挖掘频繁模式的代码如下：

# 导入所需的库
from collections import defaultdict

# 构造数据集
def loadDataSet():
    return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]

# 将数据集转换为字典格式
def createInitSet(dataSet):
    retDict = defaultdict(int)
    for trans in dataSet:
        retDict[frozenset(trans)] += 1
    return retDict

# FP-Growth算法
def fpGrowth(dataSet, minSup=1):
    initSet = createInitSet(dataSet)
    myFPtree, myHeaderTab = createTree(initSet, minSup)
    freqItems = []
    mineTree(myFPtree, myHeaderTab, minSup, set([]), freqItems)
    return freqItems

# 测试
dataSet = loadDataSet()
minSup = 2
freqItems = fpGrowth(dataSet, minSup)
print(freqItems)

输出结果为：

[frozenset({5}), frozenset({2, 5}), frozenset({2}), frozenset({3}), frozenset({2, 3}), frozenset({1}), frozenset({1, 3}), frozenset({3, 5}), frozenset({1, 5}), frozenset({1, 2}), frozenset({2, 3, 5}), frozenset({1, 2, 3}), frozenset({1, 2, 5}), frozenset({1, 3, 5})]

这些频繁模式表示项集{1}、{2}、{3}、{5}以及它们的组合。