# Local Periodic Patterns, which are patterns (sets of events) that have a periodic behavior in some non predefined
# time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some
# time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable
# lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those
# time-intervals have a minimum duration.
#
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
# from PAMI.localPeriodicPattern.basic import LPPGrowth as alg
#
# obj = alg.LPPGrowth(iFile, maxPer, maxSoPer, minDur)
#
# obj.startMine()
#
# localPeriodicPatterns = obj.getPatterns()
#
# print(f'Total number of local periodic patterns: {len(localPeriodicPatterns)}')
#
# obj.save(oFile)
#
# Df = obj.getPatternsAsDataFrame()
#
# memUSS = obj.getMemoryUSS()
#
# print(f'Total memory in USS: {memUSS}')
#
# memRSS = obj.getMemoryRSS()
#
# print(f'Total memory in RSS: {memRSS}')
#
# runtime = obj.getRuntime()
#
# print(f'Total execution time in seconds: {runtime})
#
#
__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.localPeriodicPattern.basic import abstract as _ab
from typing import List, Dict, Tuple, Set, Union, Any, Generator
[docs]
class Node:
"""
A class used to represent the node of localPeriodicPatternTree
:Attributes:
item : int
storing item of a node
parent : node
To maintain the parent of every node
child : list
To maintain the children of node
nodeLink : node
To maintain the next node of node
tidList : set
To maintain timestamps of node
:Methods:
getChild(itemName)
storing the children to their respective parent nodes
"""
def __init__(self) -> None:
self.item = -1
self.parent = None
self.child = []
self.nodeLink = None
self.tidList = set()
[docs]
def getChild(self, item: int) -> 'Node':
"""
This function is used to get child node from the parent node
:param item:
:return: if node have node of item, then return it. if node don't have return []
"""
for child in self.child:
if child.item == item:
return child
return []
[docs]
class Tree:
"""
A class used to represent the frequentPatternGrowth tree structure
:Attributes:
root : node
Represents the root node of the tree
nodeLinks : dictionary
storing last node of each item
firstNodeLink : dictionary
storing first node of each item
:Methods:
addTransaction(transaction,timeStamp)
creating transaction as a branch in frequentPatternTree
fixNodeLinks(itemName, newNode)
add newNode link after last node of item
deleteNode(itemName)
delete all node of item
createPrefixTree(path,timeStampList)
create prefix tree by path
"""
def __init__(self) -> None:
self.root = Node()
self.nodeLinks = {}
self.firstNodeLink = {}
[docs]
def addTransaction(self, transaction: List[int], tid: int) -> None:
"""
add transaction into tree
:param transaction: it represents the one transaction in database
:type transaction: list
:param tid: represents the timestamp of transaction
:type tid: list or int
"""
current = self.root
for item in transaction:
child = current.getChild(item)
if not child:
newNode = Node()
newNode.item = item
newNode.parent = current
current.child.append(newNode)
current = newNode
self.fixNodeLinks(item, newNode)
else:
current = child
current.tidList.add(tid)
[docs]
def fixNodeLinks(self, item: int, newNode: 'Node') -> None:
"""
fix node link
:param item: it represents item name of newNode
:type item: string
:param newNode: it represents node which is added
:type newNode: Node
"""
if item in self.nodeLinks:
lastNode = self.nodeLinks[item]
lastNode.nodeLink = newNode
self.nodeLinks[item] = newNode
if item not in self.firstNodeLink:
self.firstNodeLink[item] = newNode
[docs]
def deleteNode(self, item: int) -> None:
"""
delete the node from tree
:param item: it represents the item name of node
:type item: str
"""
deleteNode = self.firstNodeLink[item]
parentNode = deleteNode.parent
parentNode.child.remove(deleteNode)
parentNode.child += deleteNode.child
parentNode.tidList |= deleteNode.tidList
for child in deleteNode.child:
child.parent = parentNode
while deleteNode.nodeLink:
deleteNode = deleteNode.nodeLink
parentNode = deleteNode.parent
parentNode.child.remove(deleteNode)
parentNode.child += deleteNode.child
parentNode.tidList |= deleteNode.tidList
for child in deleteNode.child:
child.parent = parentNode
[docs]
def createPrefixTree(self, path: List[int], tidList: List[int]) -> None:
"""
create prefix tree by path
:param path: it represents path to root from prefix node
:type path: list
:param tidList: it represents tid of each item
:type tidList: list
"""
currentNode = self.root
for item in path:
child = currentNode.getChild(item)
if not child:
newNode = Node()
newNode.item = item
newNode.parent = currentNode
currentNode.child.append(newNode)
currentNode = newNode
self.fixNodeLinks(item, newNode)
else:
currentNode = child
currentNode.tidList |= tidList
[docs]
class LPPGrowth(_ab._localPeriodicPatterns):
"""
:Description:
Local Periodic Patterns, which are patterns (sets of events) that have a periodic behavior in some non predefined
time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some
time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable
lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those
time-intervals have a minimum duration.
:Reference:
Fournier-Viger, P., Yang, P., Kiran, R. U., Ventura, S., Luna, J. M.. (2020). Mining Local Periodic Patterns in
a Discrete Sequence. Information Sciences, Elsevier, to appear. [ppt] DOI: 10.1016/j.ins.2020.09.044
:param iFile: str :
Name of the Input file to mine complete set of frequent pattern's
:param oFile: str :
Name of the output file to store complete set of frequent patterns
:param minDur: str:
Minimal duration in seconds between consecutive periods of time-intervals where a pattern is continuously periodic.
:param maxPer: float:
Controls the maximum number of transactions in which any two items within a pattern can reappear.
:param maxSoPer: float:
Controls the maximum number of time periods between consecutive periods of time-intervals where a pattern is continuously periodic.
: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
Input file name or path of the input file
oFile : str
Output file name or path of the output file
maxPer : float
User defined maxPer value.
maxSoPer : float
User defined maxSoPer value.
minDur : float
User defined minDur value.
tsMin : int / date
First time stamp of input data.
tsMax : int / date
Last time stamp of input data.
startTime : float
Time when start of execution the algorithm.
endTime : float
Time when end of execution the algorithm.
finalPatterns : dict
To store local periodic patterns and its PTL.
tsList : dict
To store items and its time stamp as bit vector.
root : Tree
It is root node of transaction tree of whole input data.
PTL : dict
Storing the item and its PTL.
items : list
Storing local periodic item list.
sep: str
separator used to distinguish items from each other. The default separator is tab space.
:Methods:
findSeparator(line)
Find the separator of the line which split strings.
creteLPPlist()
Create the local periodic patterns list from input data.
createTSList()
Create the tsList as bit vector from input data.
generateLPP()
Generate 1 length local periodic pattens by tsList and execute depth first search.
createLPPTree()
Create LPPTree of local periodic item from input data.
patternGrowth(tree, prefix, prefixPFList)
Execute pattern growth algorithm. It is important function in this program.
calculatePTL(tsList)
Calculate PTL from input tsList as integer list.
calculatePTLbit(tsList)
Calculate PTL from input tsList as bit vector.
startMine()
Mining process will start from here.
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.
getLocalPeriodicPatterns()
return local periodic patterns and its PTL
save(oFile)
Complete set of local periodic patterns will be loaded in to an output file.
getPatternsAsDataFrame()
Complete set of local periodic patterns will be loaded in to a dataframe.
**Executing the code on terminal:**
-------------------------------------
Format:
>>> python3 LPPMGrowth.py <inputFile> <outputFile> <maxPer> <minSoPer> <minDur>
Examples:
>>> python3 LPPMGrowth.py sampleDB.txt patterns.txt 0.3 0.4 0.5
**Sample run of importing the code:**
----------------------------------------
.. code-block:: python
from PAMI.localPeriodicPattern.basic import LPPGrowth as alg
obj = alg.LPPGrowth(iFile, maxPer, maxSoPer, minDur)
obj.startMine()
localPeriodicPatterns = obj.getPatterns()
print(f'Total number of local periodic patterns: {len(localPeriodicPatterns)}')
obj.save(oFile)
Df = obj.getPatternsAsDataFrame()
memUSS = obj.getMemoryUSS()
print(f'Total memory in USS: {memUSS}')
memRSS = obj.getMemoryRSS()
print(f'Total memory in RSS: {memRSS}')
runtime = obj.getRuntime()
print(f'Total execution time in seconds: {runtime})
**Credits:**
--------------
The complete program was written by So Nakamura under the supervision of Professor Rage Uday Kiran.
"""
_localPeriodicPatterns__iFile = ' '
_localPeriodicPatterns__oFile = ' '
_localPeriodicPatterns__maxPer = str()
_localPeriodicPatterns__maxSoPer = str()
_localPeriodicPatterns__minDur = str()
__tsMin = 0
__tsMax = 0
_localPeriodicPatterns__startTime = float()
_localPeriodicPatterns__endTime = float()
_localPeriodicPatterns__memoryUSS = float()
_localPeriodicPatterns__memoryRSS = float()
_localPeriodicPatterns__finalPatterns = {}
__tsList = {}
__root = Tree()
__PTL = {}
__items = []
_localPeriodicPatterns__sep = ' '
__Database = []
def __creatingItemSets(self) -> None:
"""
Storing the complete transactions of the database/input file in a database variable
"""
self.__Database = []
if isinstance(self._localPeriodicPatterns__iFile, _ab._pd.DataFrame):
if self._localPeriodicPatterns__iFile.empty:
print("its empty..")
i = self._localPeriodicPatterns__iFile.columns.values.tolist()
if 'Transactions' in i:
self.__Database = self._localPeriodicPatterns__iFile['Transactions'].tolist()
if 'Patterns' in i:
self.__Database = self._localPeriodicPatterns__iFile['Patterns'].tolist()
if isinstance(self._localPeriodicPatterns__iFile, str):
if _ab._validators.url(self._localPeriodicPatterns__iFile):
data = _ab._urlopen(self._localPeriodicPatterns__iFile)
for line in data:
line.strip()
line = line.decode("utf-8")
temp = [i.rstrip() for i in line.split(self._localPeriodicPatterns__sep)]
temp = [x for x in temp if x]
self.__Database.append(temp)
else:
try:
with open(self._localPeriodicPatterns__iFile, 'r', encoding='utf-8') as f:
for line in f:
line.strip()
temp = [i.rstrip() for i in line.split(self._localPeriodicPatterns__sep)]
temp = [x for x in temp if x]
self.__Database.append(temp)
except IOError:
print("File Not Found")
quit()
def __createLPPlist(self) -> None:
"""
Create Local Periodic Pattern list from temporal data.
"""
LPPList = {}
PTL = {}
start = {}
tsPre = {}
for line in self.__Database:
soPer = ' '
self.__tsMin = int(line.pop(0))
ts = self.__tsMin
for item in line:
if item in LPPList:
per = ts - tsPre[item]
if per <= self._localPeriodicPatterns__maxPer and start == -1:
start = tsPre[item]
soPer = self._localPeriodicPatterns__maxSoPer
if start != -1:
soPer = max(0, soPer + per - self._localPeriodicPatterns__maxPer)
if soPer > self._localPeriodicPatterns__maxSoPer:
if tsPre[item] - start[item] <= self._localPeriodicPatterns__minDur:
PTL[item].add((start[item], tsPre[item]))
LPPList[item] = PTL[item]
start[item] = -1
else:
tsPre[item] = ts
start[item] = -1
LPPList[item] = set()
for line in self.__Database:
ts = int(line.pop(0))
for item in line:
if item in LPPList:
per = ts - tsPre[item]
if per <= self._localPeriodicPatterns__maxPer and start[item] == -1:
start[item] = tsPre[item]
soPer = self._localPeriodicPatterns__maxSoPer
if start[item] != -1:
soPer = max(0, soPer + per - self._localPeriodicPatterns__maxPer)
if soPer > self._localPeriodicPatterns__maxSoPer:
PTL[item].add((start[item], tsPre[item]))
LPPList[item] = PTL[item]
start[item] = -1
tsPre[item] = ts
else:
tsPre[item] = ts
start[item] = -1
LPPList[item] = set()
def __createTSList(self) -> None:
"""
Create tsList as bit vector from temporal data.
"""
# for line in self.__Database:
# count = 1
# bitVector = 0b1 << count
# bitVector = bitVector | 0b1
# self.__tsMin = int(line.pop(0))
# self.__tsList = {item: bitVector for item in line}
# count += 1
# ts = ' '
count = 1
for line in self.__Database:
bitVector = 0b1 << count
bitVector = bitVector | 0b1
# print(line)
ts = line[0]
for item in line[1:]:
if self.__tsList.get(item):
different = abs(bitVector.bit_length() - self.__tsList[item].bit_length())
self.__tsList[item] = self.__tsList[item] << different
self.__tsList[item] = self.__tsList[item] | 0b1
else:
self.__tsList[item] = bitVector
count += 1
self.__tsMax = int(ts)
for item in self.__tsList:
different = abs(bitVector.bit_length() - self.__tsList[item].bit_length())
self.__tsList[item] = self.__tsList[item] << different
self._localPeriodicPatterns__maxPer = self.__convert(self._localPeriodicPatterns__maxPer)
self._localPeriodicPatterns__maxSoPer = self.__convert(self._localPeriodicPatterns__maxSoPer)
self._localPeriodicPatterns__minDur = self.__convert(self._localPeriodicPatterns__minDur)
def __generateLPP(self) -> None:
"""
Generate local periodic items from bit vector tsList.
"""
PTL = {}
for item in self.__tsList:
PTL[item] = set()
ts = list(bin(self.__tsList[item]))
ts = ts[2:]
start = -1
currentTs = 1
soPer = ' '
tsPre = ' '
for t in ts[currentTs:]:
if t == '0':
currentTs += 1
continue
else:
tsPre = currentTs
currentTs += 1
break
for t in ts[currentTs:]:
if t == '0':
currentTs += 1
continue
else:
per = currentTs - tsPre
if per <= self._localPeriodicPatterns__maxPer and start == -1:
start = tsPre
soPer = self._localPeriodicPatterns__maxSoPer
if start != -1:
soPer = max(0, soPer + per - self._localPeriodicPatterns__maxPer)
if soPer > self._localPeriodicPatterns__maxSoPer:
if tsPre - start >= self._localPeriodicPatterns__minDur:
PTL[item].add((start, tsPre))
"""else:
bitVector = 0b1 << currentTs
different = abs(self.tsList[item].bit_length() - bitVector.bit_length())
bitVector = bitVector | 0b1
bitVector = bitVector << different
self.tsList[item] = self.tsList[item] | bitVector"""
start = -1
tsPre = currentTs
currentTs += 1
if start != -1:
soPer = max(0, soPer + self.__tsMax - tsPre - self._localPeriodicPatterns__maxPer)
if soPer > self._localPeriodicPatterns__maxSoPer and tsPre - start >= self._localPeriodicPatterns__minDur:
PTL[item].add((start, tsPre))
"""else:
bitVector = 0b1 << currentTs+1
different = abs(self.tsList[item].bit_length() - bitVector.bit_length())
bitVector = bitVector | 0b1
bitVector = bitVector << different
self.tsList[item] = self.tsList[item] | bitVector"""
if soPer <= self._localPeriodicPatterns__maxSoPer and self.__tsMax - start >= self._localPeriodicPatterns__minDur:
PTL[item].add((start, self.__tsMax))
"""else:
bitVector = 0b1 << currentTs+1
different = abs(self.tsList[item].bit_length() - bitVector.bit_length())
bitVector = bitVector | 0b1
bitVector = bitVector << different
self.tsList[item] = self.tsList[item] | bitVector"""
self.__PTL = {k: v for k, v in PTL.items() if len(v) > 0}
self.__items = list(self.__PTL.keys())
def __createLPPTree(self) -> None:
"""
Create transaction tree of local periodic item from input data.
"""
for line in self.__Database:
ts = int(line[0])
tempTransaction = [item for item in line[1:] if item in self.__items]
transaction = sorted(tempTransaction, key=lambda x: len(self.__PTL[x]), reverse=True)
self.__root.addTransaction(transaction, ts)
# for line in self.__Database:
# tid = int(transaction[0])
# tempTransaction = [item for item in transaction[1:] if item in self.__items]
# transaction = sorted(tempTransaction, key=lambda x: len(self.__PTL[x]), reverse=True)
# self.__root.addTransaction(transaction, tid)
def __patternGrowth(self, tree: 'Tree', prefix: List[int], prefixPFList: Dict[Any, Any]) -> None:
"""
Create prefix tree and prefixPFList. Store finalPatterns and its PTL.
:param tree: The root node of prefix tree.
:type tree: Node or Tree
:param prefix: Prefix item list.
:type prefix: list
:param prefixPFList: tsList of prefix patterns.
:type prefixPFList: dict or list
"""
items = list(prefixPFList)
if not prefix:
items = reversed(items)
for item in items:
prefixCopy = prefix.copy()
prefixCopy.append(item)
PFList = {}
prefixTree = Tree()
prefixNode = tree.firstNodeLink[item]
tidList = prefixNode.tidList
path = []
currentNode = prefixNode.parent
while currentNode.item != -1:
path.insert(0, currentNode.item)
currentNodeItem = currentNode.item
if currentNodeItem in PFList:
PFList[currentNodeItem] |= tidList
else:
PFList[currentNodeItem] = tidList
currentNode = currentNode.parent
prefixTree.createPrefixTree(path, tidList)
while prefixNode.nodeLink:
prefixNode = prefixNode.nodeLink
tidList = prefixNode.tidList
path = []
currentNode = prefixNode.parent
while currentNode.item != -1:
path.insert(0, currentNode.item)
currentNodeItem = currentNode.item
if currentNodeItem in PFList:
PFList[currentNodeItem] = PFList[currentNodeItem] | tidList
else:
PFList[currentNodeItem] = tidList
currentNode = currentNode.parent
prefixTree.createPrefixTree(path, tidList)
if len(prefixCopy) == 1:
self._localPeriodicPatterns__finalPatterns[prefixCopy[0]] = self.__calculatePTLbit(self.__tsList[item])
else:
self._localPeriodicPatterns__finalPatterns[tuple(prefixCopy)] = self.__calculatePTL(prefixPFList[item])
candidateItems = list(PFList)
for i in candidateItems:
PTL = self.__calculatePTL(PFList[i])
if len(PTL) == 0:
prefixTree.deleteNode(i)
del PFList[i]
if PFList:
self.__patternGrowth(prefixTree, prefixCopy, PFList)
def __calculatePTL(self, tsList: List[int]) -> set:
"""
Calculate PTL from input tsList as integer list
:param tsList: It is tsList which store time stamp as integer.
:type tsList: list
:return: PTL
"""
start = -1
PTL = set()
tsList = sorted(tsList)
tsPre = tsList[0]
soPer = ' '
for ts in tsList[1:]:
per = ts - tsPre
if per <= self._localPeriodicPatterns__maxPer and start == -1:
start = tsPre
soPer = self._localPeriodicPatterns__maxSoPer
if start != -1:
soPer = max(0, soPer + per - self._localPeriodicPatterns__maxPer)
if soPer > self._localPeriodicPatterns__maxSoPer:
if tsPre - start >= self._localPeriodicPatterns__minDur:
PTL.add((start, tsPre))
start = -1
tsPre = ts
if start != -1:
soPer = max(0, soPer + self.__tsMax - tsPre - self._localPeriodicPatterns__maxPer)
if soPer > self._localPeriodicPatterns__maxSoPer and tsPre - start >= self._localPeriodicPatterns__minDur:
PTL.add((start, tsPre))
if soPer <= self._localPeriodicPatterns__maxSoPer and self.__tsMax - start >= self._localPeriodicPatterns__minDur:
PTL.add((start, self.__tsMax))
return PTL
def __calculatePTLbit(self, tsList: List[int]) -> set:
"""
Calculate PTL from input tsList as bit vector.
:param tsList: It is tsList which store time stamp as bit vector.
:type tsList: list
:return: PTL
"""
tsList = list(bin(tsList))
tsList = tsList[2:]
start = -1
currentTs = 1
PTL = set()
tsPre = ' '
soPer = ' '
for ts in tsList[currentTs:]:
if ts == '0':
currentTs += 1
continue
else:
tsPre = currentTs
currentTs += 1
break
for ts in tsList[currentTs:]:
if ts == '0':
currentTs += 1
continue
else:
per = currentTs - tsPre
if per <= self._localPeriodicPatterns__maxPer and start == -1:
start = tsPre
soPer = self._localPeriodicPatterns__maxSoPer
if start != -1:
soPer = max(0, soPer + per - self._localPeriodicPatterns__maxPer)
if soPer > self._localPeriodicPatterns__maxSoPer:
if tsPre - start >= self._localPeriodicPatterns__minDur:
PTL.add((start, tsPre))
start = -1
tsPre = currentTs
currentTs += 1
if start != -1:
soPer = max(0, soPer + self.__tsMax - tsPre - self._localPeriodicPatterns__maxPer)
if soPer > self._localPeriodicPatterns__maxSoPer and tsPre - start >= self._localPeriodicPatterns__minDur:
PTL.add((start, tsPre))
if soPer <= self._localPeriodicPatterns__maxSoPer and self.__tsMax - start >= self._localPeriodicPatterns__minDur:
PTL.add((start, tsPre))
return PTL
def __convert(self, value: Any) -> 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]
def startMine(self) -> None:
"""
Mining process start from here.
"""
self._localPeriodicPatterns__startTime = _ab._time.time()
self._localPeriodicPatterns__finalPatterns = {}
self.__creatingItemSets()
self._localPeriodicPatterns__maxPer = self.__convert(self._localPeriodicPatterns__maxPer)
self._localPeriodicPatterns__maxSoPer = self.__convert(self._localPeriodicPatterns__maxSoPer)
self._localPeriodicPatterns__minDur = self.__convert(self._localPeriodicPatterns__minDur)
self.__createTSList()
self.__generateLPP()
self.__createLPPTree()
self.__patternGrowth(self.__root, [], self.__items)
self._localPeriodicPatterns__endTime = _ab._time.time()
process = _ab._psutil.Process(_ab._os.getpid())
self._localPeriodicPatterns__memoryUSS = float()
self._localPeriodicPatterns__memoryRSS = float()
self._localPeriodicPatterns__memoryUSS = process.memory_full_info().uss
self._localPeriodicPatterns__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._localPeriodicPatterns__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._localPeriodicPatterns__endTime - self._localPeriodicPatterns__startTime
[docs]
def getPatternsAsDataFrame(self) -> '_ab._pd.DataFrame':
"""Storing final local periodic patterns in a dataframe
:return: returning local periodic patterns in a dataframe
:rtype: pd.DataFrame
"""
dataFrame = {}
data = []
for a, b in self._localPeriodicPatterns__finalPatterns.items():
pat = str()
for i in a:
pat = pat + i + ' '
data.append([pat, b])
dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'PTL'])
return dataFrame
[docs]
def save(self, outFile: str) -> None:
"""
Complete set of local periodic patterns will be loaded in to an output file
:param outFile: name of the output file
:type outFile: csv file
"""
self._localPeriodicPatterns__oFile = outFile
writer = open(self._localPeriodicPatterns__oFile, 'w+')
for x, y in self._localPeriodicPatterns__finalPatterns.items():
pat = str()
for i in x:
pat = pat + i + '\t'
pat = pat + ":"
for i in y:
pat = pat + str(i) + '\t'
patternsAndPTL = pat.strip()
writer.write("%s \n" % patternsAndPTL)
[docs]
def getPatterns(self) -> Dict:
""" Function to send the set of local periodic patterns after completion of the mining process
:return: returning frequent patterns
:rtype: dict
"""
return self._localPeriodicPatterns__finalPatterns
[docs]
def printResults(self) -> None:
"""
This function is used to print the results
"""
print("Total number of Local Periodic 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 = LPPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], float(_ab._sys.argv[4]), _ab._sys.argv[5])
if len(_ab._sys.argv) == 5:
_ap = LPPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], float(_ab._sys.argv[4]))
_ap.startMine()
print("Total number of Local Periodic 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")