# MaxFP-Growth is one of the fundamental algorithm to discover maximal frequent patterns in a transactional database.
#
# **Importing this algorithm into a python program**
# ---------------------------------------------------------
#
# from PAMI.frequentPattern.maximal import MaxFPGrowth as alg
#
# obj = alg.MaxFPGrowth("../basic/sampleTDB.txt", "2")
#
# obj.mine()
#
# frequentPatterns = obj.getPatterns()
#
# print("Total number of Frequent Patterns:", len(frequentPatterns))
#
# obj.save("patterns")
#
# Df = obj.getPatternsAsDataFrame()
#
# memUSS = obj.getMemoryUSS()
#
# print("Total Memory in USS:", memUSS)
#
# memRSS = obj.getMemoryRSS()
#
# print("Total Memory in RSS", memRSS)
#
# run = obj.getRuntime()
#
# print("Total ExecutionTime in seconds:", run)
#
__copyright__ = """
Copyright (C) 2021 Rage Uday Kiran
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
from PAMI.frequentPattern.maximal import abstract as _ab
from deprecated import deprecated
_minSup = str()
#global maximalTree
class _Node(object):
"""
A class used to represent the node of frequentPatternTree
:Attributes:
item : int
storing item of a node
counter : list
To maintain the support of the node
parent : node
To maintain the parent of every node
children : list
To maintain the children of node
:Methods:
addChild(itemName)
storing the children to their respective parent nodes
"""
def __init__(self, item, children):
"""
Initializing the Node class
:param item: Storing the item of a node
:type item: int or None
:param children: To maintain the children of a node
:type children: dict
"""
self.item = item
self.children = children
self.counter = int()
self.parent = None
def addChild(self, node):
"""
Adding a child to the created nodes
:param node: node object
:type node: Node
"""
self.children[node.item] = node
node.parent = self
class _Tree(object):
"""
A class used to represent the frequentPatternGrowth tree structure
:Attributes:
root : Node
Represents the root node of the tree
summaries : dictionary
storing the nodes with same item name
info : dictionary
stores the support of items
:Methods:
addTransaction(transaction)
creating transaction as a branch in frequentPatternTree
addConditionalTransaction(prefixPaths, supportOfItems)
construct the conditional tree for prefix paths
condPatterns(Node)
generates the conditional patterns from tree for specific node
conditionalTransaction(prefixPaths,Support)
takes the prefixPath of a node and support at child of the path and extract the frequent items from
prefixPaths and generates prefixPaths with items which are frequent
remove(Node)
removes the node from tree once after generating all the patterns respective to the node
generatePatterns(Node)
starts from the root node of the tree and mines the frequent patterns
"""
def __init__(self):
self.root = _Node(None, {})
self.summaries = {}
self.info = {}
#self.maximalTree = _MPTree()
def addTransaction(self, transaction):
"""
Adding transactions into tree
:param transaction: represents the transaction in a database
:type transaction: list
:return: tree
"""
currentNode = self.root
for i in range(len(transaction)):
if transaction[i] not in currentNode.children:
newNode = _Node(transaction[i], {})
newNode.counter = 1
currentNode.addChild(newNode)
if transaction[i] in self.summaries:
self.summaries[transaction[i]].append(newNode)
else:
self.summaries[transaction[i]] = [newNode]
currentNode = newNode
else:
currentNode = currentNode.children[transaction[i]]
currentNode.counter += 1
def addConditionalTransaction(self, transaction, count):
"""
Loading the database into a tree
:param transaction: conditional transaction of a node
:type transaction: list
:param count: the support of conditional transaction
:type count: int
:return: conditional tree
"""
currentNode = self.root
for i in range(len(transaction)):
if transaction[i] not in currentNode.children:
newNode = _Node(transaction[i], {})
newNode.counter = count
currentNode.addChild(newNode)
if transaction[i] in self.summaries:
self.summaries[transaction[i]].append(newNode)
else:
self.summaries[transaction[i]] = [newNode]
currentNode = newNode
else:
currentNode = currentNode.children[transaction[i]]
currentNode.counter += count
def getConditionalPatterns(self, alpha):
"""
Generates all the conditional patterns of respective node
:param alpha: it represents the Node in tree
:type alpha: int
:return: conditional patterns of a node
"""
finalPatterns = []
finalSets = []
for i in self.summaries[alpha]:
set1 = i.counter
set2 = []
while i.parent.item is not None:
set2.append(i.parent.item)
i = i.parent
if len(set2) > 0:
set2.reverse()
finalPatterns.append(set2)
finalSets.append(set1)
finalPatterns, finalSets, info = self.conditionalTransactions(finalPatterns, finalSets)
return finalPatterns, finalSets, info
def conditionalTransactions(self, condPatterns, condFreq):
"""
sorting and removing the items from conditional transactions which don't satisfy minSup
:param condPatterns: conditional patterns if a node
:type condPatterns: list
:param condFreq: frequency at leaf node of conditional transaction
:type condFreq: int
:return: conditional patterns and their frequency respectively
"""
global _minSup
pat = []
tids = []
data1 = {}
for i in range(len(condPatterns)):
for j in condPatterns[i]:
if j not in data1:
data1[j] = condFreq[i]
else:
data1[j] += condFreq[i]
updatedDict = {}
updatedDict = {k: v for k, v in data1.items() if v >= _minSup}
count = 0
for p in condPatterns:
p1 = [v for v in p if v in updatedDict]
trans = sorted(p1, key=lambda x: (updatedDict.get(x), -x), reverse=True)
if len(trans) > 0:
pat.append(trans)
tids.append(condFreq[count])
count += 1
return pat, tids, updatedDict
def removeNode(self, nodeValue):
"""
To remove the node from the original tree
:param nodeValue: leaf node of tree
:type nodeValue: int
:return: tree after deleting node
"""
for i in self.summaries[nodeValue]:
del i.parent.children[nodeValue]
#i = None
def generatePatterns(self, prefix, patterns, maximalTree):
"""
Generates the patterns
:param prefix: forms the combination of items
:type prefix: str
:param patterns: the patterns we want to generate for this node
:type patterns: list
:param maximalTree: maximal frequent patterns
:type maximalTree: _MPTree()
:return: the maximal frequent patterns
:rtype: list
"""
for i in sorted(self.summaries, key=lambda x: (self.info.get(x), -x)):
pattern = prefix[:]
pattern.append(i)
condPatterns, tids, info = self.getConditionalPatterns(i)
conditional_tree = _Tree()
conditional_tree.info = info.copy()
head = pattern[:]
tail = []
for la in info:
tail.append(la)
sub = head + tail
if maximalTree.checkerSub(sub) == 1:
for pat in range(len(condPatterns)):
conditional_tree.addConditionalTransaction(condPatterns[pat], tids[pat])
if len(condPatterns) >= 1:
conditional_tree.generatePatterns(pattern, patterns, maximalTree)
else:
maximalTree.addTransaction(pattern)
patterns[tuple(pattern)] = self.info[i]
self.removeNode(i)
class _MNode(object):
"""
A class used to represent the node in maximal tree
:Attributes:
item : int
storing item of a node
children : list
To maintain the children of node
:Methods:
addChild(itemName)
storing the children to their respective parent nodes
"""
def __init__(self, item, children):
self.item = item
self.children = children
def addChild(self, node):
"""
To add the children details to a parent node
:param node: children node
:type node: _MNode
:return: adding children details to parent node
"""
self.children[node.item] = node
node.parent = self
class _MPTree(object):
"""
A class used to represent the frequentPatternGrowth tree structure
:Attributes:
root : Node
Represents the root node of the tree
summaries : dictionary
storing the nodes with same item name
:Methods:
addTransaction(transaction)
creating transaction as a branch in frequentPatternTree
addConditionalTransaction(prefixPaths, supportOfItems)
construct the conditional tree for prefix paths
checkerSub(items):
Given a set of items to the subset of them is present or not
"""
def __init__(self):
self.root = _MNode(None, {})
self.summaries = {}
def addTransaction(self, transaction):
"""
To construct the maximal frequent pattern into maximal tree
:param transaction: the maximal frequent patterns extracted till now
:type transaction: list
:return: the maximal tree
"""
currentNode = self.root
transaction.sort()
for i in range(len(transaction)):
if transaction[i] not in currentNode.children:
newNode = _MNode(transaction[i], {})
currentNode.addChild(newNode)
if transaction[i] in self.summaries:
self.summaries[transaction[i]].insert(0, newNode)
else:
self.summaries[transaction[i]] = [newNode]
currentNode = newNode
else:
currentNode = currentNode.children[transaction[i]]
def checkerSub(self, items):
"""
To check the subset of pattern present in tree
:param items: the sub frequent pattern
:type items: list
:return: checks if subset present in the tree
"""
items.sort(reverse=True)
item = items[0]
if item not in self.summaries:
return 1
else:
if len(items) == 1:
return 0
for t in self.summaries[item]:
cur = t.parent
i = 1
while cur.item is not None:
if items[i] == cur.item:
i += 1
if i == len(items):
return 0
cur = cur.parent
return 1
# Initialising the variable for maximal tree
#maximalTree = _MPTree()
[docs]
class MaxFPGrowth(_ab._frequentPatterns):
"""
:Description: MaxFP-Growth is one of the fundamental algorithm to discover maximal frequent patterns in a transactional database.
:Reference: Grahne, G. and Zhu, J., "High Performance Mining of Maximal Frequent itemSets",
http://users.encs.concordia.ca/~grahne/papers/hpdm03.pdf
:param iFile: str :
Name of the Input file to mine complete set of frequent patterns
:param oFile: str :
Name of the output file to store complete set of frequent patterns
:param minSup: int or float or str :
The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.
:param maxPer: float :
The user can specify maxPer in count or proportion of database size. If the program detects the data type of maxPer is integer, then it treats maxPer is expressed in count.
:param sep: str :
This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.
:Attributes:
startTime : float
To record the start time of the mining process
endTime : float
To record the completion time of the mining process
finalPatterns : dict
Storing the complete set of patterns in a dictionary variable
memoryUSS : float
To store the total amount of USS memory consumed by the program
memoryRSS : float
To store the total amount of RSS memory consumed by the program
Database : list
To store the transactions of a database in list
mapSupport : Dictionary
To maintain the information of item and their frequency
lno : int
it represents the total no of transactions
tree : class
it represents the Tree class
itemSetCount : int
it represents the total no of patterns
finalPatterns : dict
it represents to store the patterns
**Methods to execute code on terminal**
---------------------------------------------------------
.. code-block:: console
Format:
(.venv) $ python3 MaxFPGrowth.py <inputFile> <outputFile> <minSup>
Example Usage:
(.venv) $ python3 MaxFPGrowth.py sampleDB.txt patterns.txt 0.3
.. note:: minSup will be considered in percentage of database transactions
**Importing this algorithm into a python program**
---------------------------------------------------------
.. code-block:: python
from PAMI.frequentPattern.maximal import MaxFPGrowth as alg
obj = alg.MaxFPGrowth("../basic/sampleTDB.txt", "2")
obj.mine()
frequentPatterns = obj.getPatterns()
print("Total number of Frequent Patterns:", len(frequentPatterns))
obj.savePatterns("patterns")
Df = obj.getPatternsAsDataFrame()
memUSS = obj.getMemoryUSS()
print("Total Memory in USS:", memUSS)
memRSS = obj.getMemoryRSS()
print("Total Memory in RSS", memRSS)
run = obj.getRuntime()
print("Total ExecutionTime in seconds:", run)
**Credits:**
-------------------
The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.
"""
_startTime = float()
_endTime = float()
_minSup = str()
_maxPer = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_rank = {}
_rankdup = {}
_lno = 0
_maximalTree = str()
def _creatingItemSets(self):
"""
Storing the complete transactions of the database/input file in a database variable
"""
self._Database = []
if isinstance(self._iFile, _ab._pd.DataFrame):
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'Transactions' in i:
self._Database = self._iFile['Transactions'].tolist()
self._Database = [x.split(self._sep) for x in self._Database]
else:
print("The column name should be Transactions and each line should be separated by tab space or a seperator specified by the user")
if isinstance(self._iFile, str):
if _ab._validators.url(self._iFile):
data = _ab._urlopen(self._iFile)
for line in data:
line.strip()
line = line.decode("utf-8")
temp = [i.rstrip() for i in line.split(self._sep)]
temp = [x for x in temp if x]
self._Database.append(temp)
else:
try:
with open(self._iFile, 'r', encoding='utf-8') as f:
for line in f:
line.strip()
temp = [i.rstrip() for i in line.split(self._sep)]
temp = [x for x in temp if x]
#print(line)
self._Database.append(temp)
except IOError:
print("File Not Found")
quit()
def _frequentOneItem(self):
"""
To extract the one-length frequent itemSets
:return: 1-length frequent items
"""
_mapSupport = {}
k = 0
for tr in self._Database:
k += 1
for i in range(0, len(tr)):
if tr[i] not in _mapSupport:
_mapSupport[tr[i]] = 1
else:
_mapSupport[tr[i]] += 1
_mapSupport = {k: v for k, v in _mapSupport.items() if v >= self._minSup}
#print(len(mapSupport), self.minSup)
genList = [k for k, v in sorted(_mapSupport.items(), key=lambda x: x[1], reverse=True)]
self._rank = dict([(index, item) for (item, index) in enumerate(genList)])
return _mapSupport, genList
def _updateTransactions(self, oneLength):
"""
To sort the transactions in their support descending order and allocating ranks respectively
:param oneLength: 1-length frequent items in dictionary
:type oneLength: dict
:return: returning the sorted list
:Example: oneLength = {'a':7, 'b': 5, 'c':'4', 'd':3}
rank = {'a':0, 'b':1, 'c':2, 'd':3}
"""
list1 = []
for tr in self._Database:
list2 = []
for i in range(0, len(tr)):
if tr[i] in oneLength:
list2.append(self._rank[tr[i]])
if len(list2) >= 2:
list2.sort()
list1.append(list2)
return list1
def _buildTree(self, data, info):
"""
creating the root node as null in fp-tree and adding all transactions into tree.
:param data: updated transactions
:type data: dict
:param info: rank of items in transactions
:type info: dict
:return: fp-tree
"""
rootNode = _Tree()
rootNode.info = info.copy()
for i in range(len(data)):
rootNode.addTransaction(data[i])
return rootNode
def _convert(self, value):
"""
To convert the type of user specified minSup value
:param value: user specified minSup value
:type value: int or float or str
:return: converted type
"""
if type(value) is int:
value = int(value)
if type(value) is float:
value = (len(self._Database) * value)
if type(value) is str:
if '.' in value:
value = float(value)
value = ((len(self._Database)) * value)
else:
value = int(value)
return value
def _convertItems(self, itemSet):
"""
To convert the item ranks into their original item names
:param itemSet: itemSet or a pattern
:type itemSet: list
:return: original pattern
"""
t1 = []
for i in itemSet:
t1.append(self._rankdup[i])
return t1
[docs]
@deprecated("It is recommended to use 'mine()' instead of 'mine()' for mining process. Starting from January 2025, 'mine()' will be completely terminated.")
def startMine(self):
"""
Mining process will start from this function
"""
self.mine()
[docs]
def mine(self):
"""
Mining process will start from this function
"""
global _minSup
self._startTime = _ab._time.time()
if self._iFile is None:
raise Exception("Please enter the file path or file name:")
if self._minSup is None:
raise Exception("Please enter the Minimum Support")
self._creatingItemSets()
self._minSup = self._convert(self._minSup)
_minSup = self._minSup
generatedItems, pfList = self._frequentOneItem()
updatedTransactions = self._updateTransactions(generatedItems)
for x, y in self._rank.items():
self._rankdup[y] = x
info = {self._rank[k]: v for k, v in generatedItems.items()}
patterns = {}
self._finalPatterns = {}
self._maximalTree = _MPTree()
Tree = self._buildTree(updatedTransactions, info)
Tree.generatePatterns([], patterns, self._maximalTree)
for x, y in patterns.items():
pattern = str()
x = self._convertItems(x)
for i in x:
pattern = pattern + i + "\t"
self._finalPatterns[pattern] = y
self._endTime = _ab._time.time()
process = _ab._psutil.Process(_ab._os.getpid())
self._memoryUSS = float()
self._memoryRSS = float()
self._memoryUSS = process.memory_full_info().uss
self._memoryRSS = process.memory_info().rss
print("Maximal Frequent patterns were generated successfully using MaxFp-Growth algorithm ")
[docs]
def getMemoryUSS(self):
"""
Total amount of USS memory consumed by the mining process will be retrieved from this function
:return: returning USS memory consumed by the mining process
:rtype: float
"""
return self._memoryUSS
[docs]
def getRuntime(self):
"""
Calculating the total amount of runtime taken by the mining process
:return: returning total amount of runtime taken by the mining process
:rtype: float
"""
return self._endTime - self._startTime
[docs]
def getPatternsAsDataFrame(self):
"""
Storing final frequent patterns in a dataframe
:return: returning frequent patterns in a dataframe
:rtype: pd.DataFrame
"""
# dataframe = {}
# data = []
# for a, b in self._finalPatterns.items():
# data.append([a.replace('\t', ' '), b])
# dataframe = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
dataframe = _ab._pd.DataFrame(list([[x.replace('\t', ' '), y] for x,y in self._finalPatterns.items()]), columns=['Patterns', 'Support'])
return dataframe
[docs]
def save(self, outFile):
"""
Complete set of frequent patterns will be loaded in to an output file
:param outFile: name of the output file
:type outFile: csvfile
"""
self._oFile = outFile
writer = open(self._oFile, 'w+')
for x, y in self._finalPatterns.items():
s1 = x.strip() + ":" + str(y)
writer.write("%s \n" % s1)
[docs]
def getPatterns(self):
"""
Function to send the set of frequent patterns after completion of the mining process
:return: returning frequent patterns
:rtype: dict
"""
return self._finalPatterns
[docs]
def printResults(self):
"""
This functon is used to print the results
"""
print('Total number of Maximal Frequent Patterns: ' + str(len(self.getPatterns())))
print('Runtime: ' + str(self.getRuntime()))
print('Memory (RSS): ' + str(self.getMemoryRSS()))
print('Memory (USS): ' + str(self.getMemoryUSS()))
if __name__ == "__main__":
_ap = str()
if len(_ab._sys.argv) == 4 or len(_ab._sys.argv) == 5:
if len(_ab._sys.argv) == 5:
_ap = MaxFPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
if len(_ab._sys.argv) == 4:
_ap = MaxFPGrowth(_ab._sys.argv[1], _ab._sys.argv[3])
_ap.mine()
_ap.mine()
_ap.save(_ab._sys.argv[2])
print("Total number of Maximal Frequent Patterns:", len(_ap.getPatterns()))
print("Total Memory in USS:", _ap.getMemoryUSS())
print("Total Memory in RSS", _ap.getMemoryRSS())
print("Total ExecutionTime in ms:", _ap.getRuntime())
else:
print("Error! The number of input parameters do not match the total number of parameters provided")