# Spatial High Utility ItemSet Mining (SHUIM) [3] is an important model in data
# mining with many real-world applications. It involves finding all spatially interesting itemSets having high value
# in a quantitative spatio-temporal database.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
#
# from PAMI.highUtilitySpatialPattern.basic import HDSHUIM as alg
#
# obj=alg.HDSHUIM("input.txt","Neighbours.txt",35)
#
# obj.mine()
#
# Patterns = obj.getPatterns()
#
# print("Total number of Spatial High-Utility Patterns:", len(Patterns))
#
# 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)
#
__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.highUtilitySpatialPattern.basic import abstract as _ab
from typing import List, Dict, Tuple, Set, Union, Any, Generator
from deprecated import deprecated
class _Element:
"""
A class represents an Element of a utility list as used by the HDSHUIM algorithm.
:Attributes:
ts : int
keep tact of transaction id
snu : int
Spatial non-closed itemSet utility
remainingUtility : int
Spatial non-closed remaining utility
pu : int
prefix utility
prevPos: int
position of previous item in the list
"""
def __init__(self, ts: int, snu: int, remainingUtility: int, pu: int, prevPos: int) -> None:
self.ts = ts
self.snu = snu
self.remainingUtility = remainingUtility
self.pu = pu
self.prevPos = prevPos
class _CUList:
"""
A class represents a UtilityList as used by the HDSHUIM algorithm.
:Attributes:
item: int
item
sumSnu: long
the sum of item utilities
sumRemainingUtility: long
the sum of remaining utilities
sumCu : long
the sum of closed utilities
sumCru: long
the sum of closed remaining utilities
sumCpu: long
the sum of closed prefix utilities
elements: list
the list of elements
:Methods:
addElement(element)
Method to add an element to this utility list and update the sums at the same time.
"""
def __init__(self, item: str) -> None:
self.item = item
self.sumSnu = 0
self.sumRemainingUtility = 0
self.sumCu = 0
self.sumCru = 0
self.sumCpu = 0
self.elements = []
def addElements(self, element: _Element) -> None:
"""
A method to add new element to CUList
:param element: element to be added to CUList
:type element: Element
:return: None
"""
self.sumSnu += element.snu
self.sumRemainingUtility += element.remainingUtility
self.elements.append(element)
class _Pair:
"""
A class represent an item and its utility in a transaction
"""
def __init__(self) -> None:
self.item = 0
self.utility = 0
[docs]
class HDSHUIM(_ab._utilityPatterns):
"""
:Description:
Spatial High Utility ItemSet Mining (SHUIM) [3] is an important model in data
mining with many real-world applications. It involves finding all spatially interesting itemSets having high value
in a quantitative spatio temporal database.
:Reference:
P. Pallikila et al., "Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal
databases," 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 4925-4935,
doi: 10.1109/BigData52589.2021.9671912.
:param iFile: str :
Name of the Input file to mine complete set of High Utility Spatial patterns
:param oFile: str :
Name of the output file to store complete set of High Utility Spatial 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 minUtil: int :
Minimum utility threshold given by User
:param nFile: str :
Name of the input file to mine complete set of High Utility Spatial patterns
: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 : str
Name of the input file to mine complete set of frequent patterns
oFile : str
Name of the output file to store complete set of frequent patterns
nFile: str
Name of Neighbourhood items file
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
minUtil : int
The user given minUtil
mapFMAP: list
EUCS map of the FHM algorithm
candidates: int
candidates generated
huiCnt: int
huis created
neighbors: map
keep track of neighbours of elements
mapOfPMU: map
a map to keep track of Probable Maximum utility(PMU) of each item
:Methods:
mine()
Mining process will start 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
constructCUL(x, compactUList, st, minUtil, length, exNeighbours)
A method to construct CUL's database
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
Explore_SearchTree(prefix, uList, exNeighbours, minUtil)
A method to find all high utility itemSets
updateClosed(x, compactUList, st, exCul, newT, ex, eyTs, length)
A method to update closed values
saveItemSet(prefix, prefixLen, item, utility)
A method to save itemSets
updateElement(z, compactUList, st, exCul, newT, ex, duPrevPos, eyTs)
A method to updates vales for duplicates
**Executing the code on terminal:**
-------------------------------------
.. code-block:: console
Format:
(.venv) $ python3 HDSHUIM.py <inputFile> <outputFile> <Neighbours> <minUtil> <separator>
Example Usage:
(.venv) $ python3 HDSHUIM.py sampleTDB.txt output.txt sampleN.txt 35 ','
.. note:: minSup will be considered in percentage of database transactions
**Sample run of importing the code:**
---------------------------------------
.. code-block:: python
from PAMI.highUtilityGeoreferencedFrequentPattern.basic import HDSHUIM as alg
obj=alg.HDSHUIM("input.txt","Neighbours.txt",35)
obj.mine()
Patterns = obj.getPatterns()
print("Total number of Spatial High-Utility Patterns:", len(Patterns))
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 = " "
_nFile = " "
_minUtil = 0
_memoryUSS = float()
_memoryRSS = float()
_sep = "\t"
def __init__(self, iFile: str, nFile: str, minUtil: int, sep: str="\t") -> None:
super().__init__(iFile, nFile, minUtil, sep)
#self.oFile = None
self._startTime = 0
self._endTime = 0
self._huiCount = 0
self._candidates = 0
self._mapOfPMU = {}
self._mapFMAP = {}
self._neighbors = {}
self._finalPatterns = {}
def _compareItems(self, o1: Any, o2: Any) -> 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._mapOfPMU[o1.item] - self._mapOfPMU[o2.item]
if compare == 0:
return int(o1.item) - int(o2.item)
else:
return compare
[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:
"""
main program to start the operation
"""
self.mine()
[docs]
def mine(self) -> None:
"""
main program to start the operation
"""
minUtil = self._minUtil
self._startTime = _ab._time.time()
with open(self._nFile, 'r') as file1:
for line in file1:
line = line.split("\n")[0]
parts = line.split(self._sep)
parts = [i.strip() for i in parts]
item = parts[0]
neigh1 = list()
for i in range(1, len(parts)):
neigh1.append(parts[i])
self._neighbors[item] = set(neigh1)
with open(self._iFile, 'r') as file:
for line in file:
parts = line.split(":")
itemString = (parts[0].split("\n")[0]).split(self._sep)
utilityString = (parts[2].split("\n")[0]).split(self._sep)
transUtility = int(parts[1])
trans1 = set()
for i in range(0, len(itemString)):
trans1.add(itemString[i])
for i in range(0, len(itemString)):
item = itemString[i]
twu = self._mapOfPMU.get(item)
if twu is None:
twu = int(utilityString[i])
else:
twu += int(utilityString[i])
self._mapOfPMU[item] = twu
if self._neighbors.get(item) is None:
continue
neighbours2 = trans1.intersection(self._neighbors.get(item))
for item2 in neighbours2:
if self._mapOfPMU.get(item2) is None:
self._mapOfPMU[item2] = int(utilityString[i])
else:
self._mapOfPMU[item2] += int(utilityString[i])
listOfCUList = []
hashTable = {}
mapItemsToCUList = {}
for item in self._mapOfPMU.keys():
if self._mapOfPMU.get(item) >= minUtil:
uList = _CUList(item)
mapItemsToCUList[item] = uList
listOfCUList.append(uList)
listOfCUList.sort(key=_ab._functools.cmp_to_key(self._compareItems))
ts = 1
with open(self._iFile, 'r') as file:
for line in file:
parts = line.split(":")
items = (parts[0].split("\n")[0]).split(self._sep)
utilities = (parts[2].split("\n")[0]).split(self._sep)
ru = 0
newTwu = 0
txKey = []
revisedTrans = []
for i in range(0, len(items)):
pair = _Pair()
pair.item = items[i]
pair.utility = int(utilities[i])
if self._mapOfPMU.get(pair.item) >= minUtil:
revisedTrans.append(pair)
txKey.append(pair.item)
newTwu += pair.utility
revisedTrans.sort(key=_ab._functools.cmp_to_key(self._compareItems))
txKey1 = tuple(txKey)
if len(revisedTrans) > 0:
if txKey1 not in hashTable.keys():
hashTable[txKey1] = len(mapItemsToCUList[revisedTrans[len(revisedTrans) - 1].item].elements)
for i in range(len(revisedTrans) - 1, -1, -1):
pair = revisedTrans[i]
cuListOfItems = mapItemsToCUList.get(pair.item)
element = _Element(ts, pair.utility, ru, 0, 0)
if i > 0:
element.prevPos = len(mapItemsToCUList[revisedTrans[i - 1].item].elements)
else:
element.prevPos = -1
cuListOfItems.addElements(element)
ru += pair.utility
else:
pos = hashTable[txKey1]
ru = 0
for i in range(len(revisedTrans) - 1, -1, -1):
cuListOfItems = mapItemsToCUList[revisedTrans[i].item]
cuListOfItems.elements[pos].snu += revisedTrans[i].utility
cuListOfItems.elements[pos].remainingUtility += ru
cuListOfItems.sumSnu += revisedTrans[i].utility
cuListOfItems.sumRemainingUtility += ru
ru += revisedTrans[i].utility
pos = cuListOfItems.elements[pos].prevPos
# EUCS
for i in range(len(revisedTrans) - 1, -1, -1):
pair = revisedTrans[i]
mapFMAPItem = self._mapFMAP.get(pair.item)
if mapFMAPItem is None:
mapFMAPItem = {}
self._mapFMAP[pair.item] = mapFMAPItem
for j in range(i + 1, len(revisedTrans)):
pairAfter = revisedTrans[j]
twuSUm = mapFMAPItem.get(pairAfter.item)
if twuSUm is None:
mapFMAPItem[pairAfter.item] = newTwu
else:
mapFMAPItem[pairAfter.item] = twuSUm + newTwu
ts += 1
exNeighbours = set(self._mapOfPMU.keys())
# print(self.Neighbours)
self._ExploreSearchTree([], listOfCUList, exNeighbours, minUtil)
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
def _ExploreSearchTree(self, prefix: List[str], uList: List[_CUList], exNeighbours: set, minUtil: int) -> None:
"""
A method to find all high utility itemSets
:parm prefix: it represents all items in prefix
:type prefix :list
:parm uList:projected Utility list.
:type uList: list
:parm exNeighbours: keep track of common Neighbours
:type exNeighbours: set
:parm minUtil:user minUtil
:type minUtil:int
:return: None
"""
for i in range(0, len(uList)):
x = uList[i]
if x.item not in exNeighbours:
continue
self._candidates += 1
sortedPrefix = [0] * (len(prefix) + 1)
sortedPrefix = prefix[0:len(prefix) + 1]
sortedPrefix.append(x.item)
if (x.sumSnu + x.sumCu >= minUtil) and (x.item in exNeighbours):
self._saveItemSet(prefix, len(prefix), x.item, x.sumSnu + x.sumCu)
if x.sumSnu + x.sumCu + x.sumRemainingUtility + x.sumCru >= minUtil: # U-Prune # and (x.item in exNeighbours)):
ULIST = []
for j in range(i, len(uList)):
if (uList[j].item in exNeighbours) and (self._neighbors.get(x.item) is not None) and (
uList[j].item in self._neighbors.get(x.item)):
ULIST.append(uList[j])
exULs = self._constructCUL(x, ULIST, -1, minUtil, len(sortedPrefix), exNeighbours)
if self._neighbors.get(x.item) is not None and exNeighbours is not None:
set1 = exNeighbours.intersection(self._neighbors.get(x.item))
if exULs is None or set1 is None:
continue
self._ExploreSearchTree(sortedPrefix, exULs, set1, minUtil)
def _constructCUL(self, x: _Element, compactUList: List[_CUList], st: int, minUtil: int, length: int, exNeighbours: set) -> List[_CUList]:
"""
A method to construct CUL's database
:parm x: Compact utility list
:type x: Node
:parm compactUList:list of Compact utility lists.
:type compactUList:list
:parm st: starting pos of compactUList
:type st:int
:parm minUtil: user minUtil
:type minUtil:int
:parm length: length of x
:type length:int
:parm exNeighbours: common Neighbours
:type exNeighbours: set
:return: projected database of list X
:rtype: list or set
"""
exCul = []
lau = []
cUtil = []
eyTs = []
for i in range(0, len(compactUList)):
uList = _CUList(compactUList[i].item)
exCul.append(uList)
lau.append(0)
cUtil.append(0)
eyTs.append(0)
sz = len(compactUList) - (st + 1)
exSZ = sz
for j in range(st + 1, len(compactUList)):
mapOfTWUF = self._mapFMAP[x.item]
if mapOfTWUF is not None:
twuf = mapOfTWUF.get(compactUList[j].item)
if twuf is not None and twuf < minUtil or (not (exCul[j].item in exNeighbours)):
exCul[j] = None
exSZ = sz - 1
else:
uList = _CUList(compactUList[j].item)
exCul[j] = uList
eyTs[j] = 0
lau[j] = x.sumCu + x.sumCru + x.sumSnu + x.sumRemainingUtility
cUtil[j] = x.sumCu + x.sumCru
hashTable = {}
for ex in x.elements:
newT = []
for j in range(st + 1, len(compactUList)):
if exCul[j] is None:
continue
eyList = compactUList[j].elements
while eyTs[j] < len(eyList) and eyList[eyTs[j]].ts < ex.ts:
eyTs[j] = eyTs[j] + 1
if eyTs[j] < len(eyList) and eyList[eyTs[j]].ts == ex.ts:
newT.append(j)
else:
lau[j] = lau[j] - ex.snu - ex.remainingUtility
if lau[j] < minUtil:
exCul[j] = None
exSZ = exSZ - 1
if len(newT) == exSZ:
self._updateClosed(x, compactUList, st, exCul, newT, ex, eyTs, length)
else:
if len(newT) == 0:
continue
ru = 0
newT1 = tuple(newT)
if newT1 not in hashTable.keys():
hashTable[newT1] = len(exCul[newT[len(newT) - 1]].elements)
for i in range(len(newT) - 1, -1, -1):
cuListOfItems = exCul[newT[i]]
y = compactUList[newT[i]].elements[eyTs[newT[i]]]
element = _Element(ex.ts, ex.snu + y.snu - ex.pu, ru, ex.snu, 0)
if i > 0:
element.prevPos = len(exCul[newT[i - 1]].elements)
else:
element.prevPos = -1
cuListOfItems.addElements(element)
ru += y.snu - ex.pu
else:
dPrevPos = hashTable[newT1]
self._updateElement(x, compactUList, st, exCul, newT, ex, dPrevPos, eyTs)
for j in range(st + 1, len(compactUList)):
cUtil[j] = cUtil[j] + ex.snu + ex.remainingUtility
filter_compactUList = []
for j in range(st + 1, len(compactUList)):
if cUtil[j] < minUtil or exCul[j] is None:
continue
else:
if length > 1:
exCul[j].sumCu += compactUList[j].sumCu + x.sumCu - x.sumCpu
exCul[j].sumCru += compactUList[j].sumCru
exCul[j].sumCpu += x.sumCu
filter_compactUList.append(exCul[j])
return filter_compactUList
def _updateClosed(self, x: _Element, compactUList: List[_CUList], st: int, exCul: List[_CUList], newT: List[int], ex: _Element, eyTs: List[int], length: int) -> None:
"""
A method to update closed values
:parm x: Compact utility list.
:type x: list
:parm compactUList:list of Compact utility lists.
:type compactUList:list
:parm st: starting pos of compactUList
:type st:int
:parm newT:transaction to be updated
:type newT:list
:parm ex: element ex
:type ex:element
:parm eyTs:list of tss
:type eyTs:ts
:parm length: length of x
:type length:int
:return: None
"""
remainingUtility = 0
for j in range(len(newT) - 1, -1, -1):
ey = compactUList[newT[j]]
eyy = ey.elements[eyTs[newT[j]]]
exCul[newT[j]].sumCu += ex.snu + eyy.snu - ex.pu
exCul[newT[j]].sumCru += remainingUtility
exCul[newT[j]].sumCpu += ex.snu
remainingUtility = remainingUtility + eyy.snu - ex.pu
def _updateElement(self, z: _Element, compactUList: List[_CUList], st: int, exCul: List[_CUList], newT: List[int], ex: _Element, duPrevPos: int, eyTs: List[int]) -> None:
"""
A method to updates vales for duplicates
:parm z: Compact utility list
:type z: list
:parm compactUList:list of Compact utility lists
:type compactUList:list
:parm st: starting pos of compactUList
:type st:int
:parm exCul:list of compactUList
:type exCul:list
:parm newT:transaction to be updated
:type newT:list
:parm ex: element ex
:type ex:element
:parm duPrevPos: position of z in exCul
:type duPrevPos:int
:parm eyTs:list of tss
:type eyTs:ts
:return: None
"""
remainingUtility = 0
pos = duPrevPos
for j in range(len(newT) - 1, -1, -1):
ey = compactUList[newT[j]]
eyy = ey.elements[eyTs[newT[j]]]
exCul[newT[j]].elements[pos].snu += ex.snu + eyy.snu - ex.pu
exCul[newT[j]].sumSnu += ex.snu + eyy.snu - ex.pu
exCul[newT[j]].elements[pos].remainingUtility += remainingUtility
exCul[newT[j]].sumRemainingUtility += remainingUtility
exCul[newT[j]].elements[pos].pu += ex.snu
remainingUtility = remainingUtility + eyy.snu - ex.pu
pos = exCul[newT[j]].elements[pos].prevPos
def _saveItemSet(self, prefix: List[str], prefixLen: int, item: str, utility: int) -> None:
"""
A method to save itemSets
:parm prefix: it represents all items in prefix
:type prefix :list
:parm item:item
:type item: int
:parm utility:utility of itemSet
:type utility:int
:return: None
"""
self._huiCount += 1
res = str()
for i in range(0, prefixLen):
res += str(prefix[i]) + "\t"
res += str(item)
res1 = str(utility)
self._finalPatterns[res] = res1
[docs]
def getPatternsAsDataFrame(self) -> Dict[str, str]:
"""
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'])
return dataFrame
[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
"""
return self._finalPatterns
[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
:return: None
"""
self.oFile = outFile
writer = open(self.oFile, 'w+')
for x, y in self._finalPatterns.items():
patternsAndSupport = x.strip() + ":" + str(y)
writer.write("%s \n" % patternsAndSupport)
[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
[docs]
def printResults(self) -> None:
"""
This function is used to print the results
"""
print("Total number of Spatial High Utility Patterns:", len(self.getPatterns()))
print("Total Memory in USS:", self.getMemoryUSS())
print("Total Memory in RSS", self.getMemoryRSS())
print("Total ExecutionTime in seconds:", 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: # to include a user specified separator
_ap = HDSHUIM(_ab._sys.argv[1], _ab._sys.argv[3], int(_ab._sys.argv[4]), _ab._sys.argv[5])
if len(_ab._sys.argv) == 5: # to consider "\t" as a separator
_ap = HDSHUIM(_ab._sys.argv[1], _ab._sys.argv[3], int(_ab._sys.argv[4]))
_ap.mine()
_ap.mine()
print("Total number of Spatial High-Utility 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:
for i in [100000, 500000]:
_ap = HDSHUIM('/Users/Likhitha/Downloads/mushroom_main_2000.txt',
'/Users/Likhitha/Downloads/mushroom_neighbors_2000.txt', i, ' ')
_ap.mine()
_ap.mine()
print("Total number of Spatial High Utility Patterns:", len(_ap.getPatterns()))
print("Total Memory in USS:", _ap.getMemoryUSS())
print("Total Memory in RSS", _ap.getMemoryRSS())
print("Total ExecutionTime in seconds:", _ap.getRuntime())
print("Error! The number of input parameters do not match the total number of parameters provided")