FP-growth算法实际应用python代码

FP-growth算法是一种用于频繁模式挖掘的算法，常用于数据挖掘、市场分析和推荐系统等领域。下面是使用Python实现FP-growth算法的代码：

首先，需要导入相应的库：

from collections import defaultdict
from itertools import chain
from typing import List, Tuple

接着，定义一些常量：

# 定义FP树节点
class FPTreeNode:
    def __init__(self, item=None, count=1, parent=None):
        self.item = item
        self.count = count
        self.parent = parent
        self.children = defaultdict(FPTreeNode)
        
# 定义FP树
class FPTree:
    def __init__(self, transactions, support, root_value, root_count):
        self.frequent_items = self.find_frequent_items(transactions, support)
        self.headers = self.build_header_table(self.frequent_items)
        self.root = self.build_fptree(transactions, root_value, root_count, self.frequent_items, self.headers)

# 定义FP-growth算法
class FPGrowth:
    def __init__(self, min_support=0.5, min_confidence=0.5):
        self.min_support = min_support
        self.min_confidence = min_confidence

# 定义函数：寻找频繁项集
    def find_frequent_items(self, transactions, support):
        items = defaultdict(lambda: 0)
        for transaction in transactions:
            for item in transaction:
                items[item] += 1

        # 去除不符合最小支持度的项
        items = dict((item, support) for item, support in items.items()
                     if support >= support * len(transactions))

        # 返回频繁项集
        return items

接着，实现构建FP树的函数：

# 定义函数：构建FP树
    def build_fptree(self, transactions, root_value, root_count, frequent_items, headers):
        root = FPTreeNode(item=root_value, count=root_count)
        for transaction in transactions:
            sorted_items = sorted([item for item in transaction if item in frequent_items], key=lambda item: frequent_items[item], reverse=True)
            if len(sorted_items) > 0:
                self.insert_tree(sorted_items, root, headers)
        return root

# 定义函数：插入节点到FP树中
    def insert_tree(self, items, node, headers):
        if items[0] in node.children:
            child = node.children[items[0]]
        else:
            child = FPTreeNode(item=items[0], parent=node)
            headers[items[0]].append(child)
            node.children[items[0]] = child

        if len(items) > 1:
            self.insert_tree(items[1:], child, headers)

        child.count += 1

最后，实现FP-growth算法的主函数：

# 定义函数：寻找频繁模式
    def find_frequent_patterns(self, transactions):
        if not transactions:
            return None

        # 构建FP树
        support = self.min_support
        root_value = 'null'
        root_count = len(transactions)
        fp_tree = FPTree(transactions, support, root_value, root_count)

        # 寻找频繁项集和条件模式基
        frequent_patterns = defaultdict(int)
        conditional_patterns = defaultdict(list)
        self.mine_patterns(fp_tree, fp_tree.header_table, frequent_patterns, conditional_patterns)

        # 返回频繁模式
        return frequent_patterns

    # 定义函数：挖掘频繁项集
    def mine_patterns(self, tree, headers, frequent_patterns, conditional_patterns):
        sorted_items = [item[0] for item in sorted(headers.items(), key=lambda x: x[1][0].count)]
        for item in sorted_items:
            base_patterns = [path(item_node) for item_node in headers[item]]
            frequent_patterns.update({tuple(pattern): headers[item][0].count for pattern in base_patterns})

            conditional_tree = self.build_conditional_tree(base_patterns, headers[item])
            if conditional_tree:
                self.mine_patterns(conditional_tree, conditional_tree.header_table, frequent_patterns, conditional_patterns)

这样，我们就完成了使用Python实现FP-growth算法的代码。