fp算法python代码

### FP-Growth Algorithm Implementation in Python

The FP-Growth (Frequent Pattern Growth) algorithm is an efficient and scalable method for mining frequent itemsets. Below is a simplified version of how one might implement this algorithm using Python:

#### Importing Necessary Libraries
To start with, several libraries need importing to facilitate operations such as data manipulation.

from collections import defaultdict
import itertools

#### Defining Helper Functions
Helper functions assist in constructing the FP-tree structure and extracting patterns from it.

def create_fp_tree(data_set, min_support=1):
    header_table = {}
    # Count frequency of each item
    for transaction in data_set:
        for item in transaction:
            header_table[item] = header_table.get(item, 0) + data_set[transaction]

    # Remove items not meeting minimum support level
    for k in list(header_table):  
        if header_table[k] < min_support: 
            del(header_table[k])

    freq_itemset = set(header_table.keys())
    
    if len(freq_itemset) == 0: 
        return None, None
    
    for k in header_table:
        header_table[k] = [header_table[k], None]
        
    ret_tree = TreeNode('Null Set', 1, None)

    for tran_set, count in list(data_set.items()):
        local_d = {}
        for item in tran_set:
            if item in freq_itemset:
                local_d[item] = header_table[item][0]
                
        if len(local_d) > 0:
            ordered_items = [v[0] for v in sorted(local_d.items(), key=lambda p: p[1], reverse=True)]
            update_tree(ordered_items, ret_tree, header_table, count)
            
    return ret_tree, header_table


class TreeNode:
    def __init__(self, name_value, num_occur, parent_node):
        self.name = name_value
        self.count = num_occur
        self.node_link = None
        self.parent = parent_node      
        self.children = {} 

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


def update_header(node_to_test, target_node):
    while node_to_test.node_link != None:
        node_to_test = node_to_test.node_link
    node_to_test.node_link = target_node
    

def update_tree(items, in_tree, header_table, count):
    if items[0] in in_tree.children:
        in_tree.children[items[0]].inc(count)
    else:
        in_tree.children[items[0]] = TreeNode(items[0], count, in_tree)
        if header_table[items[0]][1] == None:
            header_table[items[0]][1] = in_tree.children[items[0]]
        else:
            update_header(header_table[items[0]][1], in_tree.children[items[0]])
            
    if len(items) > 1:
        update_tree(items[1::], in_tree.children[items[0]], header_table, count)

This implementation covers creating an FP tree based on transactions provided by users or applications[^1]. It also includes classes and methods necessary for managing nodes within the tree structure efficiently.

For completeness, additional components would be required to fully utilize the power of FP-growth including pattern generation through conditional pattern bases and extraction algorithms that traverse the constructed trees looking for significant associations between elements present therein.