# RSFPGrowth algorithm is used to find all items with relative support from given dataset
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
#
# from PAMI.relativeFrequentPattern import RSFPGrowth as alg
#
# obj = alg.RSFPGrowth(iFile, minSup, __minRatio)
#
# obj.mine()
#
# frequentPatterns = obj.getPatterns()
#
# print("Total number of Frequent Patterns:", len(frequentPatterns))
#
# obj.save(oFile)
#
# 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/>.
Copyright (C) 2021 Rage Uday Kiran
"""
#from PAMI.relativeFrequentPattern.basic import abstract as _ab
from typing import List, Dict, Tuple, Set, Union, Any, Generator
#import pandas as pd
from PAMI.relativeFrequentPattern.basic import abstract as _ab
import pandas as pd
from deprecated import deprecated
class _Node:
"""
A class used to represent the node of frequent Pattern tree
:Attributes:
itemId: int
storing item of a node
counter: int
To maintain the support of node
parent: node
To maintain the parent of every node
child: list
To maintain the children of node
nodeLink : node
Points to the node with same itemId
:Methods:
getChild(itemName)
returns the node with same itemName from frequent Pattern tree
"""
def __init__(self) -> None:
self.itemId = -1
self.counter = 1
self.parent = None
self.child = []
self.nodeLink = None
def getChild(self, itemName: int) -> Union[None, '_Node']:
"""
Retrieving the child from the tree
:param itemName: name of the child
:type itemName: list
:return: returns the node with same itemName from frequentPatternTree
:rtype: None or Node
"""
for i in self.child:
if i.itemId == itemName:
return i
return None
class _Tree:
"""
A class used to represent the frequentPatternGrowth tree structure
:Attributes:
headerList : list
storing the list of items in tree sorted in ascending of their supports
mapItemNodes : dictionary
storing the nodes with same item name
mapItemLastNodes : dictionary
representing the map that indicates the last node for each item
root : Node
representing the root Node in a tree
:Methods:
createHeaderList(items,minSup)
takes items only which are greater than minSup and sort the items in ascending order
addTransaction(transaction)
creating transaction as a branch in frequentPatternTree
fixNodeLinks(item,newNode)
To create the link for nodes with same item
printTree(Node)
gives the details of node in frequentPatternGrowth tree
addPrefixPath(prefix,port,minSup)
It takes the items in prefix pattern whose support is >=minSup and construct a subtree
"""
def __init__(self) -> None:
self.headerList = []
self.mapItemNodes = {}
self.mapItemLastNodes = {}
self.root = _Node()
def addTransaction(self, transaction: List[int]) -> None:
"""
Adding transaction into tree
:param transaction: it represents the one transaction in database
:type transaction: list
:return: None
"""
# This method taken a transaction as input and returns the tree
current = self.root
for i in transaction:
child = current.getChild(i)
if not child:
newNode = _Node()
newNode.itemId = i
newNode.parent = current
current.child.append(newNode)
self.fixNodeLinks(i, newNode)
current = newNode
else:
child.counter += 1
current = child
def fixNodeLinks(self, item: int, newNode: '_Node') -> None:
"""
Fixing node link for the newNode that inserted into frequentPatternTree
:param item: it represents the item of newNode
:type item: int
:param newNode: it represents the newNode that inserted in frequentPatternTree
:type newNode: Node
:return: None
"""
if item in self.mapItemLastNodes.keys():
lastNode = self.mapItemLastNodes[item]
lastNode.nodeLink = newNode
self.mapItemLastNodes[item] = newNode
if item not in self.mapItemNodes.keys():
self.mapItemNodes[item] = newNode
def printTree(self, root: '_Node') -> None:
"""
Print the details of Node in frequentPatternTree
:param root: it represents the Node in frequentPatternTree
:type root: Node
:return: None
"""
# this method is used print the details of tree
if not root.child:
return
else:
for i in root.child:
print(i.itemId, i.counter, i.parent.itemId)
self.printTree(i)
def createHeaderList(self, __mapSupport: Dict[int, int], minSup: float) -> None:
"""
To create the headerList
:param __mapSupport: it represents the items with their supports
:type __mapSupport: dictionary
:param minSup: it represents the minSup
:param minSup: float
:return: None
"""
# the frequentPatternTree always maintains the header table to start the mining from leaf nodes
t1 = []
for x, y in __mapSupport.items():
if y >= minSup:
t1.append(x)
__itemSetBuffer = [k for k, v in sorted(__mapSupport.items(), key=lambda _x: _x[1], reverse=True)]
self.headerList = [i for i in t1 if i in __itemSetBuffer]
def addPrefixPath(self, prefix: List['_Node'], __mapSupportBeta: Dict[int, int], minSup: float) -> None:
"""
To construct the conditional tree with prefix paths of a node in frequentPatternTree
:param prefix: it represents the prefix items of a Node
:type prefix: list
:param __mapSupportBeta: it represents the items with their supports
:param __mapSupportBeta: dictionary
:param minSup: to check the item meets with minSup
:param minSup: float
"""
# this method is used to add prefix paths in conditional trees of frequentPatternTree
pathCount = prefix[0].counter
current = self.root
prefix.reverse()
for i in range(0, len(prefix) - 1):
pathItem = prefix[i]
if __mapSupportBeta.get(pathItem.itemId) >= minSup:
child = current.getChild(pathItem.itemId)
if not child:
newNode = _Node()
newNode.itemId = pathItem.itemId
newNode.parent = current
newNode.counter = pathCount
current.child.append(newNode)
current = newNode
self.fixNodeLinks(pathItem.itemId, newNode)
else:
child.counter += pathCount
current = child
[docs]
class RSFPGrowth(_ab._frequentPatterns):
"""
:Description: Algorithm to find all items with relative support from given dataset
:Reference: 'Towards Efficient Discovery of Frequent Patterns with Relative Support' R. Uday Kiran and
Masaru Kitsuregawa, http://comad.in/comad2012/pdf/kiran.pdf
:param iFile: str :
Name of the Input file to mine complete set of Relative frequent pattern's
:param oFile: str :
Name of the output file to store complete set of Relative frequent patterns
:param minSup: str:
Controls the minimum number of transactions in which every item must appear in a database.
:param minRS: float:
Controls the minimum number of transactions in which at least one time within a pattern must appear in a database.
: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:
iFile : file
Name of the Input file to mine complete set of frequent patterns
oFile : file
Name of the output file to store complete set of frequent patterns
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
startTime:float
To record the start time of the mining process
endTime : float
To record the completion time of the mining process
minSup : float
The user given minSup
minRS : float
The user given minRS
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
itemSetBuffer : list
it represents the store the items in mining
maxPatternLength : int
it represents the constraint for pattern length
:Methods:
mine()
Mining process will start from here
getFrequentPatterns()
Complete set of patterns will be retrieved with this function
save(oFile)
Complete set of frequent patterns will be loaded in to a output file
getPatternsAsDataFrame()
Complete set of frequent patterns will be loaded in to a dataframe
getmemoryUSS()
Total amount of USS memory consumed by the mining process will be retrieved from this function
getMemoryRSS()
Total amount of RSS memory consumed by the mining process will be retrieved from this function
getRuntime()
Total amount of runtime taken by the mining process will be retrieved from this function
check(line)
To check the delimiter used in the user input file
creatingItemSets(fileName)
Scans the dataset or dataframes and stores in list format
frequentOneItem()
Extracts the one-frequent patterns from transactions
saveAllCombination(tempBuffer,s,position,prefix,prefixLength)
Forms all the combinations between prefix and tempBuffer lists with support(s)
saveItemSet(pattern,support)
Stores all the frequent patterns with their respective support
frequentPatternGrowthGenerate(frequentPatternTree,prefix,port)
Mining the frequent patterns by forming conditional frequentPatternTrees to particular prefix item.
__mapSupport represents the 1-length items with their respective support
**Methods to execute code on terminal**
----------------------------------------------
.. code-block:: console
Format:
(.venv) $python3 RSFPGrowth.py <inputFile> <outputFile> <minSup> <__minRatio>
Example Usage :
(.venv) $python3 python3 RSFPGrowth.py sampleDB.txt patterns.txt 0.23 0.2
.. note:: maxPer and minPS will be considered in percentage of database transactions
**Importing this algorithm into a python program**
-----------------------------------------------------
.. code-block:: python
from PAMI.relativeFrequentPattern import RSFPGrowth as alg
obj = alg.RSFPGrowth(iFile, minSup, __minRatio)
obj.mine()
frequentPatterns = obj.getPatterns()
print("Total number of Frequent Patterns:", len(frequentPatterns))
obj.save(oFile)
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 Sai Chitra.B under the supervision of Professor Rage Uday Kiran.
"""
__startTime = float()
__endTime = float()
_minSup = str()
_minRS = float()
__finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
__memoryUSS = float()
__memoryRSS = float()
__Database = []
__mapSupport = {}
__lno = 0
__tree = _Tree()
__itemSetBuffer = None
__fpNodeTempBuffer = []
__itemSetCount = 0
__maxPatternLength = 1000
__oFile = None
def __init__(self, iFile: Union[str, pd.DataFrame], minSup: Union[int, float, str], minRS: float, sep: str='\t') -> None:
super().__init__(iFile, minSup, minRS, sep)
self.__finalPatterns = {}
def __creatingItemSets(self) -> None:
"""
Storing the complete transactions of the __Database/input file in a __Database variable
:return: None
"""
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()
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]
self.__Database.append(temp)
except IOError:
print("File Not Found")
quit()
def __frequentOneItem(self) -> None:
"""
Generating One frequent items sets
:return: None
"""
self.__mapSupport = {}
for i in self.__Database:
for j in i:
if j not in self.__mapSupport:
self.__mapSupport[j] = 1
else:
self.__mapSupport[j] += 1
def __saveItemSet(self, prefix: List[int], prefixLength: int, support: int, ratio: float) -> None:
"""
To save the frequent patterns mined form frequentPatternTree
:param prefix: the frequent pattern
:type prefix: list
:param prefixLength: the length of a frequent pattern
:type prefixLength: int
:param support: the support of a pattern
:type support: int
:return: None
"""
sample = []
for i in range(prefixLength):
sample.append(prefix[i])
self.__itemSetCount += 1
self.__finalPatterns[tuple(sample)] = str(support) + " : " + str(ratio)
def __saveAllCombinations(self, tempBuffer: List['_Node'], s: int, position: int, prefix: List[int], prefixLength: int) -> None:
"""
Generating all the combinations for items in single branch in frequentPatternTree
:param tempBuffer: items in a list.
:type tempBuffer: list
:param s: support at leaf node of a branch
:param position: the length of a tempBuffer
:type position: int
:param prefix: it represents the list of leaf node
:type prefix: list
:param prefixLength: the length of prefix
:type prefixLength: int
"""
max1 = 1 << position
for i in range(1, max1):
newPrefixLength = prefixLength
for j in range(position):
isSet = i & (1 << j)
if isSet > 0:
prefix.insert(newPrefixLength, tempBuffer[j].itemId)
newPrefixLength += 1
ratio = s / self.__mapSupport[self.__getMinItem(prefix, newPrefixLength)]
if ratio >= self._minRS:
self.__saveItemSet(prefix, newPrefixLength, s, ratio)
def __frequentPatternGrowthGenerate(self, frequentPatternTree: '_Tree', prefix: List[int], prefixLength: int, __mapSupport: Dict[int, int], minConf: float) -> None:
"""
Mining the fp tree
:param frequentPatternTree: it represents the frequentPatternTree
:type frequentPatternTree: class Tree
:param prefix: it represents an empty list and store the patterns that are mined
:type prefix: list
:param prefixLength: the length of prefix
:type prefixLength: int
:param __mapSupport : it represents the support of item
:type __mapSupport : dictionary
"""
singlePath = True
position = 0
s = 0
if len(frequentPatternTree.root.child) > 1:
singlePath = False
else:
currentNode = frequentPatternTree.root.child[0]
while True:
if len(currentNode.child) > 1:
singlePath = False
break
self.__fpNodeTempBuffer.insert(position, currentNode)
s = currentNode.counter
position += 1
if len(currentNode.child) == 0:
break
currentNode = currentNode.child[0]
if singlePath is True:
self.__saveAllCombinations(self.__fpNodeTempBuffer, s, position, prefix, prefixLength)
else:
for i in reversed(frequentPatternTree.headerList):
item = i
support = __mapSupport[i]
CminSup = max(self._minSup, support * self._minRS)
betaSupport = support
prefix.insert(prefixLength, item)
max1 = self.__getMinItem(prefix, prefixLength)
if self.__mapSupport[max1] > self.__mapSupport[item]:
max1 = item
ratio = support / self.__mapSupport[max1]
if ratio >= self._minRS:
self.__saveItemSet(prefix, prefixLength + 1, betaSupport, ratio)
if prefixLength + 1 < self.__maxPatternLength:
prefixPaths = []
path = frequentPatternTree.mapItemNodes.get(item)
__mapSupportBeta = {}
while path is not None:
if path.parent.itemId != -1:
prefixPath = [path]
pathCount = path.counter
parent1 = path.parent
if __mapSupport.get(parent1.itemId) >= CminSup:
while parent1.itemId != -1:
mins = CminSup
if __mapSupport.get(parent1.itemId) >= mins:
prefixPath.append(parent1)
if __mapSupportBeta.get(parent1.itemId) is None:
__mapSupportBeta[parent1.itemId] = pathCount
else:
__mapSupportBeta[parent1.itemId] = __mapSupportBeta[
parent1.itemId] + pathCount
parent1 = parent1.parent
else:
break
prefixPaths.append(prefixPath)
path = path.nodeLink
__treeBeta = _Tree()
for k in prefixPaths:
__treeBeta.addPrefixPath(k, __mapSupportBeta, self._minSup)
if len(__treeBeta.root.child) > 0:
__treeBeta.createHeaderList(__mapSupportBeta, self._minSup)
self.__frequentPatternGrowthGenerate(__treeBeta, prefix, prefixLength + 1, __mapSupportBeta,
minConf)
def __convert(self, value: Union[int, float, str]) -> float:
"""
to convert the type of user specified __minSup value
:param value: user specified __minSup value
: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
[docs]
@deprecated("It is recommended to use mine() instead of mine() for mining process")
def startMine(self) -> None:
"""
Main program to start the operation
:return: None
"""
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)
self._minRS = float(self._minRS)
self.__frequentOneItem()
self.__finalPatterns = {}
self.__mapSupport = {k: v for k, v in self.__mapSupport.items() if v >= self._minSup}
__itemSetBuffer = [k for k, v in sorted(self.__mapSupport.items(), key=lambda x: x[1], reverse=True)]
for i in self.__Database:
transaction = []
for j in i:
if j in __itemSetBuffer:
transaction.append(j)
transaction.sort(key=lambda val: self.__mapSupport[val], reverse=True)
self.__tree.addTransaction(transaction)
self.__tree.createHeaderList(self.__mapSupport, self._minSup)
if len(self.__tree.headerList) > 0:
self.__itemSetBuffer = []
self.__frequentPatternGrowthGenerate(self.__tree, self.__itemSetBuffer, 0, self.__mapSupport, self._minRS)
print("Relative support frequent patterns were generated successfully using RSFPGrowth algorithm")
self.__endTime = _ab._time.time()
process = _ab._psutil.Process(_ab._os.getpid())
self.__memoryRSS = float()
self.__memoryUSS = float()
self.__memoryUSS = process.memory_full_info().uss
self.__memoryRSS = process.memory_info().rss
[docs]
def mine(self) -> None:
"""
Main program to start the operation
:return: None
"""
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)
self._minRS = float(self._minRS)
self.__frequentOneItem()
self.__finalPatterns = {}
self.__mapSupport = {k: v for k, v in self.__mapSupport.items() if v >= self._minSup}
__itemSetBuffer = [k for k, v in sorted(self.__mapSupport.items(), key=lambda x: x[1], reverse=True)]
for i in self.__Database:
transaction = []
for j in i:
if j in __itemSetBuffer:
transaction.append(j)
transaction.sort(key=lambda val: self.__mapSupport[val], reverse=True)
self.__tree.addTransaction(transaction)
self.__tree.createHeaderList(self.__mapSupport, self._minSup)
if len(self.__tree.headerList) > 0:
self.__itemSetBuffer = []
self.__frequentPatternGrowthGenerate(self.__tree, self.__itemSetBuffer, 0, self.__mapSupport,
self._minRS)
print("Relative support frequent patterns were generated successfully using RSFPGrowth algorithm")
self.__endTime = _ab._time.time()
process = _ab._psutil.Process(_ab._os.getpid())
self.__memoryRSS = float()
self.__memoryUSS = float()
self.__memoryUSS = process.memory_full_info().uss
self.__memoryRSS = process.memory_info().rss
[docs]
def getMemoryUSS(self) -> float:
"""
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
def __getMinItem(self, prefix: List[str], prefixLength: int) -> str:
"""
Returns the minItem from prefix
"""
minItem = prefix[0]
for i in range(prefixLength):
if self.__mapSupport[minItem] > self.__mapSupport[prefix[i]]:
minItem = prefix[i]
return minItem
[docs]
def getRuntime(self) -> float:
"""
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) -> pd.DataFrame:
"""
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():
pattern = str()
for i in a:
pattern = pattern + i + " "
data.append([pattern, b])
dataframe = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
return dataframe
[docs]
def save(self, outFile: str) -> None:
"""
Complete set of frequent patterns will be loaded in to an output file
:param outFile: name of the output file.
:type outFile: file
:return: None
"""
self.__oFile = outFile
writer = open(self.__oFile, 'w+')
for x, y in self.__finalPatterns.items():
pattern = str()
for i in x:
pattern = pattern + i + "\t"
s1 = pattern.strip() + ": " + str(y)
writer.write("%s \n" % s1)
[docs]
def getPatterns(self) -> Dict[str, str]:
"""
Function to send the set of frequent patterns after completion of the mining process
:return: returning frequent patterns
:rtype: dict
"""
res = dict()
for x, y in self.__finalPatterns.items():
pattern = str()
for i in x:
pattern = pattern + i + "\t"
s1 = str(y)
res[pattern] = s1
return res
[docs]
def printResults(self) -> None:
"""
This function is used to print the results
:return: None
"""
print("Total number of Relative Frequent Patterns:", len(self.getPatterns()))
print("Total Memory in USS:", self.getMemoryUSS())
print("Total Memory in RSS", self.getMemoryRSS())
print("Total ExecutionTime in ms:", self.getRuntime())
if __name__ == "__main__":
_ap = str()
if len(_ab._sys.argv) == 5 or len(_ab._sys.argv) == 6:
if len(_ab._sys.argv) == 6:
_ap = RSFPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5])
if len(_ab._sys.argv) == 5:
_ap = RSFPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
_ap.mine()
print("Total number of Frequent Patterns:", len(_ap.getPatterns()))
_ap.save(_ab._sys.argv[2])
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")