# FCPGrowth is the algorithm to discover Correlated Fuzzy-frequent patterns in a transactional database.
# it is based on traditional fuzzy frequent pattern mining.
#
# **Importing this algorithm into a python program**
# -------------------------------------------------------
#
# from PAMI.fuzzyCorrelatedPattern.basic import FCPGrowth as alg
#
# obj = alg.FCPGrowth("input.txt",2,0.4)
#
# obj.mine()
#
# correlatedFuzzyFrequentPatterns = obj.getPatterns()
#
# print("Total number of Correlated Fuzzy Frequent Patterns:", len(correlatedFuzzyFrequentPatterns))
#
# obj.save("output")
#
# 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)
#
from PAMI.fuzzyCorrelatedPattern.basic import abstract as _ab
from typing import List, Dict, Tuple, Set, Union, Any, Generator
from deprecated import deprecated
class _FFList:
"""
A class represent a Fuzzy List of an element
:Attributes:
item: int
the item name
sumIUtil: float
the sum of utilities of a fuzzy item in database
sumRUtil: float
the sum of resting values of a fuzzy item in database
elements: list
a list of elements contain tid,Utility and resting values of element in each transaction
:Methods:
addElement(element)
Method to add an element to this fuzzy list and update the sums at the same time.
printElement(e)
Method to print elements
"""
def __init__(self, itemName: str, region: str) -> None:
self.item = itemName
self.region = region
self.sumIUtil = 0.0
self.sumRUtil = 0.0
self.elements = []
def addElement(self, element: 'Element') -> None:
"""
A Method that add a new element to FFList
:param element: an element to be added to FFList
:param element: Element
"""
self.sumIUtil += element.IUtils
self.sumRUtil += element.RUtils
self.elements.append(element)
[docs]
class Element:
"""
A class represents an Element of a fuzzy list
:Attributes:
tid : int
keep tact of transaction id
IUtils: float
the utility of a fuzzy item in the transaction
RUtil : float
the neighbourhood resting value of a fuzzy item in the transaction
"""
def __init__(self, tid: int, IUtil: float, RUtil: float) -> None:
self.tid = tid
self.IUtils = IUtil
self.RUtils = RUtil
class _Regions:
"""
A class calculate the region value
:Attributes:
low : int
low region value
middle: int
middle region value
high : int
high region values
"""
def __init__(self, item: str, quantity: int, regionsNumber: int, mapOfRegions: Dict) -> None:
self.low = 0
self.middle = 0
self.high = 0
if regionsNumber == 3:
if 0 < quantity <= 1:
self.low = 1
self.high = 0
self.middle = 0
t1 = (item, 'L')
if t1 not in mapOfRegions.keys():
mapOfRegions[t1] = 1
else:
temp = mapOfRegions[t1]
mapOfRegions[t1] = temp + 1
elif 1 <= quantity < 6:
self.low = float((-0.2 * quantity) + 1.2)
self.middle = float((0.2 * quantity) - 0.2)
self.high = 0
t1 = (item, 'L')
if t1 not in mapOfRegions.keys():
mapOfRegions[t1] = 1
else:
temp = mapOfRegions[t1]
mapOfRegions[t1] = temp + 1
t1 = (item, 'M')
if t1 not in mapOfRegions.keys():
mapOfRegions[t1] = 1
else:
temp = mapOfRegions[t1]
mapOfRegions[t1] = temp + 1
elif 6 <= quantity <= 11:
self.low = 0
self.middle = float((-0.2 * quantity) + 2.2)
self.high = float((0.2 * quantity) - 1.2)
t1 = (item, 'M')
if t1 not in mapOfRegions.keys():
mapOfRegions[t1] = 1
else:
temp = mapOfRegions[t1]
mapOfRegions[t1] = temp + 1
t1 = (item, 'H')
if t1 not in mapOfRegions.keys():
mapOfRegions[t1] = 1
else:
temp = mapOfRegions[t1]
mapOfRegions[t1] = temp + 1
else:
self.low = 0
self.middle = 0
self.high = 1
t1 = (item, 'H')
if t1 not in mapOfRegions.keys():
mapOfRegions[t1] = 1
else:
temp = mapOfRegions[t1]
mapOfRegions[t1] = temp + 1
class _Pair:
"""
A class to store item and it's quantity together
"""
def __init__(self) -> None:
"""
A Class to Store item and its quantity together
"""
self.item = 0
self.quantity = 0
self.region = 'N'
[docs]
class FCPGrowth(_ab._corelatedFuzzyFrequentPatterns):
"""
:Description: FCPGrowth is the algorithm to discover Correlated Fuzzy-frequent patterns in a transactional database.
it is based on traditional fuzzy frequent pattern mining.
:Reference: Lin, N.P., & Chueh, H. (2007). Fuzzy correlation rules mining.
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.416.6053&rep=rep1&type=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. Otherwise, it will be treated as float.
: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 minAllConf: float :
The user can specify minAllConf values within the range (0, 1).
: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 fuzzy spatial frequent patterns
oFile : file
Name of the oFile file to store complete set of fuzzy spatial frequent patterns
minSup : int
The user given support
minAllConf: float
user Specified minAllConf( should be in range 0 and 1)
memoryRSS : float
To store the total amount of RSS memory consumed by the program
startTimeTime:float
To record the startTime time of the mining process
endTime:float
To record the completion time of the mining process
itemsCnt: int
To record the number of fuzzy spatial itemSets generated
mapItemsLowSum: map
To keep track of low region values of items
mapItemsMidSum: map
To keep track of middle region values of items
mapItemsHighSum: map
To keep track of high region values of items
mapItemSum: map
To keep track of sum of Fuzzy Values of items
mapItemRegions: map
To Keep track of fuzzy regions of item
jointCnt: int
To keep track of the number of FFI-list that was constructed
BufferSize: int
represent the size of Buffer
itemBuffer list
to keep track of items in buffer
:Methods:
startTimeMine()
Mining process will startTime from here
getPatterns()
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
getRatio(self, prefix, prefixLen, item)
Method to calculate the ration of itemSet
convert(value):
To convert the given user specified value
FSFIMining( prefix, prefixLen, fsFim, minSup)
Method generate FFI from prefix
construct(px, py)
A function to construct Fuzzy itemSet from 2 fuzzy itemSets
findElementWithTID(uList, tid)
To find element with same tid as given
WriteOut(prefix, prefixLen, item, sumIUtil,ratio)
To Store the patten
**Executing the code on terminal :**
------------------------------------------
.. code-block:: console
Format:
(.venv) $ python3 FCPGrowth.py <inputFile> <outputFile> <minSup> <minAllConf> <sep>
Example Usage:
(.venv) $ python3 FCPGrowth.py sampleTDB.txt output.txt 2 0.2
.. note:: minSup will be considered in percentage of database transactions
**Sample run of importing the code:**
-----------------------------------------
.. code-block:: python
from PAMI.fuzzyCorrelatedPattern.basic import FCPGrowth as alg
obj = alg.FCPGrowth("input.txt",2,0.4)
obj.mine()
correlatedFuzzyFrequentPatterns = obj.getPatterns()
print("Total number of Correlated Fuzzy Frequent Patterns:", len(correlatedFuzzyFrequentPatterns))
obj.save("output")
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 B.Sai Chitra under the supervision of Professor Rage Uday Kiran.
"""
_startTime = float()
_endTime = float()
_minSup = str()
_maxPer = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_minAllConf = 0.0
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
def __init__(self, iFile: str, minSup: int, minAllConf: float, sep: str="\t") -> None:
super().__init__(iFile, minSup, minAllConf, sep)
self._temp = {}
self._mapItemRegionSum = {}
self._itemsCnt = 0
self._mapItemsLowSum = {}
self._mapItemsMidSum = {}
self._mapItemsHighSum = {}
self._mapItemSum = {}
self._mapItemRegions = {}
self._joinsCnt = 0
self._BufferSize = 200
self._itemSetBuffer = []
self._finalPatterns = {}
self._dbLen = 0
self._transactions = []
self._fuzzyValues = []
def _compareItems(self, o1: _FFList, o2: _FFList) -> int:
"""
A Function that sort all FFI-list in ascending order of Support
:param o1: First FFI-list
:type o1: _FFList
:param o2: Second FFI-list
:type o1: _FFList
:return: Comparision Value
:rtype: int
"""
compare = self._mapItemSum[o1.item] - self._mapItemSum[o2.item]
if compare == 0:
# return int(o1.item) - int(o2.item)
return 1
else:
return compare
def _findElementWithTID(self, uList: _FFList, tid: int) -> Element:
"""
To find element with same tid as given
:param uList: fuzzyList
:type uList: FFI-List
:param tid: transaction id
:type tid: int
:return: element eith tid as given
:rtype: element if exists or None
"""
List_ = uList.elements
first = 0
last = len(List_) - 1
while first <= last:
mid = (first + last) >> 1
if List_[mid].tid < tid:
first = mid + 1
elif List_[mid].tid > tid:
last = mid - 1
else:
return List_[mid]
return None
def _convert(self, value: Union[int, float, str]) -> float:
"""
To convert the given user specified value
:param value: user specified value
:return: converted value
:rtype: float
"""
if type(value) is int:
value = int(value)
if type(value) is float:
value = (len(self._transactions) * value)
if type(value) is str:
if '.' in value:
value = float(value)
value = (len(self._transactions) * value)
else:
value = int(value)
return value
def _creatingItemSets(self) -> None:
"""
Storing the complete transactions of the database/input file in a database variable
:return: None
"""
self._transactions, self._fuzzyValues = [], []
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._transactions = self._iFile['Transactions'].tolist()
if 'fuzzyValues' in i:
self._fuzzyValues = self._iFile['Utilities'].tolist()
# print(self.Database)
if isinstance(self._iFile, str):
if _ab._validators.url(self._iFile):
data = _ab._urlopen(self._iFile)
for line in data:
line = line.decode("utf-8")
line = line.split("\n")[0]
parts = line.split(":")
parts[0] = parts[0].strip()
parts[1] = parts[1].strip()
items = parts[0].split(self._sep)
quantities = parts[1].split(self._sep)
self._transactions.append([x for x in items])
self._fuzzyValues.append([x for x in quantities])
else:
try:
with open(self._iFile, 'r', encoding='utf-8') as f:
for line in f:
line = line.split("\n")[0]
parts = line.split(":")
items = parts[0].split()
quantities = parts[1].split()
self._transactions.append([x for x in items])
self._fuzzyValues.append([x for x in quantities])
except IOError:
print("File Not Found")
quit()
[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) -> None:
"""
Frequent pattern mining process will startTime from here
"""
self.mine()
[docs]
def mine(self) -> None:
"""
Frequent pattern mining process will startTime from here
"""
self._startTime = _ab._time.time()
self._creatingItemSets()
for tr in range(len(self._transactions)):
items = self._transactions[tr]
quantities = self._fuzzyValues[tr]
for i in range(0, len(items)):
item = items[i]
regions = _Regions(item, float(quantities[i]), 3, self._mapItemRegionSum)
if item in self._mapItemsLowSum.keys():
low = self._mapItemsLowSum[item]
low += regions.low
self._mapItemsLowSum[item] = low
else:
self._mapItemsLowSum[item] = regions.low
if item in self._mapItemsMidSum.keys():
mid = self._mapItemsMidSum[item]
mid += regions.middle
self._mapItemsMidSum[item] = mid
else:
self._mapItemsMidSum[item] = regions.middle
if item in self._mapItemsHighSum.keys():
high = self._mapItemsHighSum[item]
high += regions.high
self._mapItemsHighSum[item] = high
else:
self._mapItemsHighSum[item] = regions.high
listOfFFIList = []
mapItemsToFFLIST = {}
self._minSup = self._convert(self._minSup)
#minSup = self._minSup
self._minAllConf = float(self._minAllConf)
for item1 in self._mapItemsLowSum.keys():
item = item1
region = 'N'
low = self._mapItemsLowSum[item]
mid = self._mapItemsMidSum[item]
high = self._mapItemsHighSum[item]
if low >= mid and low >= high:
self._mapItemSum[item] = low
self._mapItemRegions[item] = "L"
region = 'L'
elif mid >= low and mid >= high:
self._mapItemSum[item] = mid
self._mapItemRegions[item] = "M"
region = 'M'
elif high >= low and high >= mid:
self._mapItemRegions[item] = "H"
region = 'H'
self._mapItemSum[item] = high
if self._mapItemSum[item] >= self._minSup:
fuList = _FFList(item, region)
mapItemsToFFLIST[item] = fuList
listOfFFIList.append(fuList)
listOfFFIList.sort(key=_ab._functools.cmp_to_key(self._compareItems))
tid = 0
for tr in range(len(self._transactions)):
items = self._transactions[tr]
quantities = self._fuzzyValues[tr]
revisedTransaction = []
for i in range(0, len(items)):
pair = _Pair()
pair.item = items[i]
regions = _Regions(pair.item, float(quantities[i]), 3, self._temp)
item = pair.item
if self._mapItemSum[item] >= self._minSup:
if self._mapItemRegions[pair.item] == "L":
pair.quantity = regions.low
pair.region = 'L'
elif self._mapItemRegions[pair.item] == "M":
pair.region = 'M'
pair.quantity = regions.middle
elif self._mapItemRegions[pair.item] == "H":
pair.quantity = regions.high
pair.region = 'H'
if pair.quantity > 0:
revisedTransaction.append(pair)
revisedTransaction.sort(key=_ab._functools.cmp_to_key(self._compareItems))
for i in range(len(revisedTransaction) - 1, -1, -1):
pair = revisedTransaction[i]
remainUtil = 0
for j in range(len(revisedTransaction) - 1, i - 1, -1):
remainUtil += revisedTransaction[j].quantity
if pair.quantity > remainUtil:
remainingUtility = pair.quantity
else:
remainingUtility = remainUtil
if mapItemsToFFLIST.get(pair.item) is not None:
FFListOfItem = mapItemsToFFLIST[pair.item]
element = Element(tid, pair.quantity, remainingUtility)
FFListOfItem.addElement(element)
tid += 1
self._FSFIMining(self._itemSetBuffer, 0, listOfFFIList, self._minSup)
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("Fuzzy Correlated Patterns Successfully generated using FCPGrowth algorithms")
def _FSFIMining(self, prefix: List[_FFList], prefixLen: int, FSFIM: List[_FFList], minSup: float) -> None:
"""
Generates FFSI from prefix
:param prefix: the prefix patterns of FFSI
:type prefix: len
:param prefixLen: the length of prefix
:type prefixLen: int
:param FSFIM: the Fuzzy list of prefix itemSets
:type FSFIM: list
:param minSup: the minimum support of
:type minSup:int
"""
for i in range(0, len(FSFIM)):
X = FSFIM[i]
if X.sumIUtil >= minSup:
ratio = self._getRatio(prefix, X)
if ratio >= self._minAllConf:
self._WriteOut(prefix, prefixLen, X, ratio)
if X.sumRUtil >= minSup:
exULs = []
for j in range(i + 1, len(FSFIM)):
Y = FSFIM[j]
exULs.append(self._construct(X, Y))
self._joinsCnt += 1
self._itemSetBuffer.insert(prefixLen, X)
self._FSFIMining(self._itemSetBuffer, prefixLen + 1, exULs, minSup)
def _construct(self, px: _FFList, py: _FFList) -> _FFList:
"""
A function to construct a new Fuzzy itemSet from 2 fuzzy itemSets
:param px:the itemSet px
:type px:FFI-List
:param py:itemSet py
:type py:FFI-List
:return :the itemSet of pxy(px and py)
:rtype :FFI-List
"""
pxyUL = _FFList(py.item, py.region)
for ex in px.elements:
ey = self._findElementWithTID(py, ex.tid)
if ey is None:
continue
eXY = Element(ex.tid, min([ex.IUtils, ey.IUtils], key=lambda x: float(x)), ey.RUtils)
pxyUL.addElement(eXY)
return pxyUL
[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
[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
def _getRatio(self, prefix: List[_FFList], item: _FFList) -> float:
"""
Method to calculate the ration of itemSet
:param prefix: prefix of itemSet
:type prefix: list
:param item: the last item
:type item: FFList
:return : correlated ratio
:rtype: float
"""
res = 1.0
#n = prefixLen
for i in prefix:
if self._mapItemRegionSum.get((i.item, i.region)) is not None and res < self._mapItemRegionSum[(i.item, i.region)]:
res = self._mapItemRegionSum[(i.item, i.region)]
if self._mapItemRegionSum.get((item.item, item.region)) is not None and res < self._mapItemRegionSum[
(item.item, item.region)]:
res = self._mapItemRegionSum[(item.item, item.region)]
return item.sumIUtil / res
def _WriteOut(self, prefix: List[_FFList], prefixLen: int, item: _FFList, ratio: float) -> None:
"""
To Store the patten
:param prefix: prefix of itemSet
:type prefix: list
:param prefixLen: length of prefix
:type prefixLen: int
:param item: the last item
:type item: FFList
:param ratio: the ratio of itemSet
:type ratio: float
:return: None
"""
self._itemsCnt += 1
res = ""
for i in range(0, prefixLen):
res += str(prefix[i].item) + "." + str(prefix[i].region) + '\t'
res += str(item.item) + "." + str(item.region)
#res1 = str(item.sumIUtil) + " : " + str(ratio) + "\n"
self._finalPatterns[res] = [item.sumIUtil, ratio]
[docs]
def getPatterns(self) -> Dict[str, List[float]]:
"""
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 getPatternsAsDataFrame(self) -> _ab._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():
data.append([a.replace('\t', ' '), b[0], b[1]])
dataframe = _ab._pd.DataFrame(data, columns=['Patterns', 'Support', 'Confidence'])
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: csv file
"""
self._oFile = outFile
writer = open(self._oFile, 'w+')
for x, y in self._finalPatterns.items():
patternsAndSupport = x.strip() + ":" + str(y[0]) + ":" + str(y[1])
writer.write("%s \n" % patternsAndSupport)
[docs]
def printResults(self) -> None:
"""
This function is used to print the result
"""
print("Total number of Fuzzy Correlated 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())
[docs]
def main():
inputFile = 'https://u-aizu.ac.jp/~udayrage/datasets/fuzzyDatabases/Fuzzy_T10I4D100K.csv'
minimumSupportCount=1200 #Users can also specify this constraint between 0 to 1.
ratioExample=0.8
seperator='\t'
obj = FCPGrowth(inputFile, minimumSupportCount,ratioExample,seperator) #initialize
obj.mine()
if __name__ == "__main__":
main()
_ap = str()
if len(_ab._sys.argv) == 5 or len(_ab._sys.argv) == 6:
if len(_ab._sys.argv) == 6:
_ap = FCPGrowth(_ab._sys.argv[1], int(_ab._sys.argv[3]), float(_ab._sys.argv[4]), _ab._sys.argv[5])
if len(_ab._sys.argv) == 5:
_ap = FCPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], float(_ab._sys.argv[4]))
_ap.mine()
_ap.mine()
print("Total number of Fuzzy Correlated 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 seconds:", _ap.getRuntime())
else:
print("Error! The number of input parameters do not match the total number of parameters provided")