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from __future__ import division, print_function
import numpy as np
import itertools
class FPTreeNode():
def __init__(self, item=None, support=1):
# 'Value' of the item
self.item = item
# Number of times the item occurs in a
# transaction = support
# Child nodes in the FP Growth Tree
self.children = {}
class FPGrowth():
"""A method for determining frequent itemsets in a transactional database.
This is done by building a so called FP Growth tree, which can then be mined
to collect the frequent itemsets. More effective than Apriori for large transactional
min_sup: float
The minimum fraction of transactions an itemets needs to
occur in to be deemed frequent
def __init__(self, min_sup=0.3):
self.min_sup = min_sup
# The root of the initial FP Growth Tree
self.tree_root = None
# Prefixes of itemsets in the FP Growth Tree
self.prefixes = {}
self.frequent_itemsets = []
# Count the number of transactions that contains item.
def _calculate_support(self, item, transactions):
count = 0
for transaction in transactions:
if item in transaction:
count += 1
support = count
return support
def _get_frequent_items(self, transactions):
""" Returns a set of frequent items. An item is determined to
be frequent if there are atleast min_sup transactions that contains
it. """
# Get all unique items in the transactions
unique_items = set(
item for transaction in transactions for item in transaction)
items = []
for item in unique_items:
sup = self._calculate_support(item, transactions)
if sup >= self.min_sup:
items.append([item, sup])
# Sort by support - Highest to lowest
items.sort(key=lambda item: item[1], reverse=True)
frequent_items = [[el[0]] for el in items]
# Only return the items
return frequent_items
def _insert_tree(self, node, children):
""" Recursive method which adds nodes to the tree. """
if not children:
# Create new node as the first item in children list
child_item = children[0]
child = FPTreeNode(item=child_item)
# If parent already contains item => increase the support
if child_item in node.children:
node.children[child.item].support += 1
node.children[child.item] = child
# Execute _insert_tree on the rest of the children list
# from the new node
self._insert_tree(node.children[child.item], children[1:])
def _construct_tree(self, transactions, frequent_items=None):
if not frequent_items:
# Get frequent items sorted by support
frequent_items = self._get_frequent_items(transactions)
unique_frequent_items = list(
set(item for itemset in frequent_items for item in itemset))
# Construct the root of the FP Growth tree
root = FPTreeNode()
for transaction in transactions:
# Remove items that are not frequent according to
# unique_frequent_items
transaction = [item for item in transaction if item in unique_frequent_items]
transaction.sort(key=lambda item: frequent_items.index([item]))
self._insert_tree(root, transaction)
return root
def print_tree(self, node=None, indent_times=0):
""" Recursive method which prints the FP Growth Tree """
if not node:
node = self.tree_root
indent = " " * indent_times
print ("%s%s:%s" % (indent, node.item,
for child_key in node.children:
child = node.children[child_key]
self.print_tree(child, indent_times + 1)
def _is_prefix(self, itemset, node):
""" Makes sure that the first item in itemset is a child of node
and that every following item in itemset is reachable via that path """
for item in itemset:
if not item in node.children:
return False
node = node.children[item]
return True
def _determine_prefixes(self, itemset, node, prefixes=None):
""" Recursive method that adds prefixes to the itemset by traversing the
FP Growth Tree"""
if not prefixes:
prefixes = []
# If the current node is a prefix to the itemset
# add the current prefixes value as prefix to the itemset
if self._is_prefix(itemset, node):
itemset_key = self._get_itemset_key(itemset)
if not itemset_key in self.prefixes:
self.prefixes[itemset_key] = []
self.prefixes[itemset_key] += [{"prefix": prefixes, "support": node.children[itemset[0]].support}]
for child_key in node.children:
child = node.children[child_key]
# Recursive call with child as new node. Add the child item as potential
# prefix.
self._determine_prefixes(itemset, child, prefixes + [child.item])
def _get_itemset_key(self, itemset):
""" Determines the look of the hashmap key for self.prefixes
List of more strings than one gets joined by '-' """
if len(itemset) > 1:
itemset_key = "-".join(itemset)
itemset_key = str(itemset[0])
return itemset_key
def _determine_frequent_itemsets(self, conditional_database, suffix):
# Calculate new frequent items from the conditional database
# of suffix
frequent_items = self._get_frequent_items(conditional_database)
cond_tree = None
if suffix:
cond_tree = self._construct_tree(conditional_database, frequent_items)
# Output new frequent itemset as the suffix added to the frequent
# items
self.frequent_itemsets += [el + suffix for el in frequent_items]
# Find larger frequent itemset by finding prefixes
# of the frequent items in the FP Growth Tree for the conditional
# database.
self.prefixes = {}
for itemset in frequent_items:
# If no suffix (first run)
if not cond_tree:
cond_tree = self.tree_root
# Determine prefixes to itemset
self._determine_prefixes(itemset, cond_tree)
conditional_database = []
itemset_key = self._get_itemset_key(itemset)
# Build new conditional database
if itemset_key in self.prefixes:
for el in self.prefixes[itemset_key]:
# If support = 4 => add 4 of the corresponding prefix set
for _ in range(el["support"]):
# Create new suffix
new_suffix = itemset + suffix if suffix else itemset
self._determine_frequent_itemsets(conditional_database, suffix=new_suffix)
def find_frequent_itemsets(self, transactions, suffix=None, show_tree=False):
self.transactions = transactions
# Build the FP Growth Tree
self.tree_root = self._construct_tree(transactions)
if show_tree:
print ("FP-Growth Tree:")
self._determine_frequent_itemsets(transactions, suffix=None)
return self.frequent_itemsets
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