基于python实现FP-Growth算法，要求测试集定义“交易”为订单ID，“购买商品”为一次订单内购买的商品，

好的，首先我们需要了解什么是FP-Growth算法。

FP-Growth算法是一种用于挖掘频繁项集的算法，它通过构建FP-Tree树来快速发现频繁项集。FP-Tree树是一种前缀树，它将所有的事务按照出现频率从高到低排序，并将它们插入到树中，同时保留每个事务中出现的所有项。

基于这个思想，我们可以实现一个简单的FP-Growth算法。下面是代码实现：

class FPNode:
    def __init__(self, item=None, count=1):
        self.item = item
        self.count = count
        self.parent = None
        self.link = None
        self.children = {}

    def add_child(self, child):
        if child.item not in self.children:
            self.children[child.item] = child
            child.parent = self
        else:
            self.children[child.item].count += child.count

    def get_child(self, item):
        return self.children.get(item, None)

    def increment(self):
        self.count += 1

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

def build_tree(transactions, minsup):
    header_table = {}
    for transaction in transactions:
        for item in transaction:
            header_table[item] = header_table.get(item, 0) + transactions[transaction]
    for k in list(header_table):
        if header_table[k] < minsup:
            del(header_table[k])
    freq_items = set(header_table.keys())
    if len(freq_items) == 0:
        return None, None
    for k in header_table:
        header_table[k] = [header_table[k], None]
    root = FPNode()
    for transaction, count in transactions.items():
        ordered_items = [x for x in transaction if x in freq_items]
        ordered_items.sort(key=lambda x: header_table[x][0], reverse=True)
        if len(ordered_items) > 0:
            current_node = root
            for item in ordered_items:
                current_node = current_node.get_child(item) or current_node.add_child(FPNode(item, 0))
                current_node.increment()
                header_table[item][1] = header_table[item][1] or current_node
    return root, header_table

def find_prefix_path(tree_node):
    path = {}
    while tree_node is not None:
        prefix_path = []
        current_node = tree_node
        while current_node.parent is not None:
            prefix_path.append(current_node.item)
            current_node = current_node.parent
        if len(prefix_path) > 1:
            path[frozenset(prefix_path[1:])] = tree_node.count
        tree_node = tree_node.link
    return path

def mine_tree(tree, header_table, minsup, pre_fix, freq_item_list):
    sorted_items = [x[0] for x in sorted(header_table.items(), key=lambda x: x[1][0])]
    for item in sorted_items:
        new_freq_set = pre_fix.copy()
        new_freq_set.add(item)
        freq_item_list.append(new_freq_set)
        conditional_pattern_bases = find_prefix_path(header_table[item][1])
        conditional_tree, conditional_header = build_tree(conditional_pattern_bases, minsup)
        if conditional_header is not None:
            mine_tree(conditional_tree, conditional_header, minsup,
                      new_freq_set, freq_item_list)

def fp_growth(transactions, minsup):
    tree, header_table = build_tree(transactions, minsup)
    freq_item_list = []
    mine_tree(tree, header_table, minsup, set(), freq_item_list)
    return freq_item_list

这里的`transactions`是一个字典，键为订单ID，值为该订单中购买的商品列表。我们需要将其转换成一个列表，每个元素为一个购买商品的集合。下面是一个样例：

transactions = {
    1: ['A', 'B', 'C', 'D'],
    2: ['B', 'C', 'E'],
    3: ['A', 'B', 'C', 'E'],
    4: ['B', 'D', 'E'],
    5: ['A', 'B', 'C', 'D', 'E'],
    6: ['A', 'C', 'E']
}

transaction_list = [[item] for order, items in transactions.items() for item in items]

然后我们就可以调用`fp_growth`函数来找出频繁项集了：

freq_itemsets = fp_growth(transaction_list, 3)
for itemset in freq_itemsets:
    print(itemset)

这里的`minsup`参数表示最小支持度，我们可以根据需要进行调整。