# PPF_DFS is algorithm to mine the partial periodic frequent patterns.
#
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
# from PAMI.partialPeriodicFrequentPattern.basic import PPF_DFS as alg
#
# obj = alg.PPF_DFS(iFile, minSup)
#
# obj.startMine()
#
# frequentPatterns = obj.getPatterns()
#
# print("Total number of Frequent Patterns:", len(frequentPatterns))
#
# obj.save(oFile)
#
# Df = obj.getPatternInDataFrame()
#
# 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.partialPeriodicFrequentPattern.basic.abstract import *
[docs]
class PPF_DFS(partialPeriodicPatterns):
"""
:Description: PPF_DFS is algorithm to mine the partial periodic frequent patterns.
:References: (Has to be added)
: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 minSup: str:
The user can specify minSup either in count or proportion of database size.
:param minPR: str:
Controls the maximum number of transactions in which any two items within a pattern can reappear.
:param maxPer: str:
Controls the maximum number of transactions in which any two items within a pattern can reappear.
: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
input file path
oFile : file
output file name
minSup : float
user defined minSup
maxPer : float
user defined maxPer
minPR : float
user defined minPR
tidlist : dict
it stores tids each item
last : int
it represents last time stamp in database
lno : int
number of line in database
mapSupport : dict
to maintain the information of item and their frequency
finalPatterns : dict
it represents to store the patterns
runTime : float
storing the total runtime of the mining process
memoryUSS : float
storing the total amount of USS memory consumed by the program
memoryRSS : float
storing the total amount of RSS memory consumed by the program
:Methods:
getPer_Sup(tids)
caluclate ip / (sup+1)
getPerSup(tids)
caluclate ip
oneItems(path)
scan all lines in database
save(prefix,suffix,tidsetx)
save prefix pattern with support and periodic ratio
Generation(prefix, itemsets, tidsets)
Userd to implement prefix class equibalence method to generate the periodic patterns recursively
startMine()
Mining process will start from here
getPartialPeriodicPatterns()
Complete set of patterns will be retrieved with this function
save(ouputFile)
Complete set of frequent patterns will be loaded in to an ouput file
getPatternsAsDataFrame()
Complete set of frequent patterns will be loaded in to an ouput file
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
**Executing code on Terminal:**
----------------------------------
Format:
>>> python3 PPF_DFS.py <inputFile> <outputFile> <minSup> <maxPer> <minPR>
Examples:
>>> python3 PPF_DFS.py sampleDB.txt patterns.txt 10 10 0.5
**Sample run of the importing code:**
---------------------------------------
... code-block:: python
from PAMI.partialPeriodicFrequentpattern.basic import PPF_DFS as alg
obj = alg.PPF_DFS(iFile, minSup)
obj.startMine()
frequentPatterns = obj.getPatterns()
print("Total number of Frequent Patterns:", len(frequentPatterns))
obj.save(oFile)
Df = obj.getPatternInDataFrame()
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 S. Nakamura under the supervision of Professor Rage Uday Kiran.\n
"""
__path = ' '
_partialPeriodicPatterns__iFile = ' '
_partialPeriodicPatterns__oFile = ' '
_partialPeriodicPatterns__sep = str()
_partialPeriodicPatterns__minSup = str()
_partialPeriodicPatterns__maxPer = str()
_partialPeriodicPatterns__minPR = str()
__tidlist = {}
__last = 0
__lno = 0
__mapSupport = {}
_partialPeriodicPatterns__finalPatterns = {}
__runTime = float()
_partialPeriodicPatterns__memoryUSS = float()
_partialPeriodicPatterns__memoryRSS = float()
_partialPeriodicPatterns__startTime = float()
_partialPeriodicPatterns__endTime = float()
__Database = []
def __creatingItemSets(self):
"""
Storing the complete transactions of the database/input file in a database variable
"""
self.__Database = []
if isinstance(self._partialPeriodicPatterns__iFile, pd.DataFrame):
timeStamp, data = [], []
if self._partialPeriodicPatterns__iFile.empty:
print("its empty..")
i = self._partialPeriodicPatterns__iFile.columns.values.tolist()
if 'ts' or 'TS' in i:
timeStamp = self._partialPeriodicPatterns__iFile['timeStamps'].tolist()
if 'Transactions' in i:
data = self._partialPeriodicPatterns__iFile['Transactions'].tolist()
if 'Patterns' in i:
data = self._partialPeriodicPatterns__iFile['Patterns'].tolist()
for i in range(len(data)):
tr = [timeStamp[i]]
tr.append(data[i])
self.__Database.append(tr)
self.__lno = len(self.__Database)
if isinstance(self._partialPeriodicPatterns__iFile, str):
if validators.url(self._partialPeriodicPatterns__iFile):
data = urlopen(self._partialPeriodicPatterns__iFile)
for line in data:
self.__lno += 1
line = line.decode("utf-8")
temp = [i.rstrip() for i in line.split(self._partialPeriodicPatterns__sep)]
temp = [x for x in temp if x]
self.__Database.append(temp)
else:
try:
with open(self._partialPeriodicPatterns__iFile, 'r', encoding='utf-8') as f:
for line in f:
self.__lno += 1
temp = [i.rstrip() for i in line.split(self._partialPeriodicPatterns__sep)]
temp = [x for x in temp if x]
self.__Database.append(temp)
except IOError:
print("File Not Found")
quit()
def __getPer_Sup(self, tids):
"""
calculate ip / (sup+1)
:param tids: it represent tid list
:type tids: list
:return: ip / (sup+1)
"""
# print(lno)
tids = list(set(tids))
tids.sort()
per = 0
sup = 0
cur = 0
if len(tids) == 0:
return 0
if abs(0 - tids[0]) <= self._partialPeriodicPatterns__maxPer:
sup += 1
for j in range(len(tids) - 1):
i = j + 1
per = abs(tids[i] - tids[j])
if (per <= self._partialPeriodicPatterns__maxPer):
sup += 1
cur = tids[j]
if abs(self.__last - tids[len(tids) - 1]) <= self._partialPeriodicPatterns__maxPer:
sup += 1
if sup == 0:
return 0
return sup / (len(tids) + 1)
def _partialPeriodicPatterns__getPerSup(self, tids):
"""
calculate ip of a pattern
:param tids: tid list of the pattern
:type tids: list
:return: ip
"""
# print(lno)
tids = list(set(tids))
tids.sort()
per = 0
sup = 0
cur = 0
if len(tids) == 0:
return 0
if abs(0 - tids[0]) <= self._partialPeriodicPatterns__maxPer:
sup += 1
for j in range(len(tids) - 1):
i = j + 1
per = abs(tids[i] - tids[j])
if (per <= self._partialPeriodicPatterns__maxPer):
sup += 1
if abs(tids[len(tids) - 1] - self.__last) <= self._partialPeriodicPatterns__maxPer:
sup += 1
if sup == 0:
return 0
return sup
def __convert(self, value):
"""
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
def __oneItems(self, path):
"""
scan all lines of database and create support list
:param path: it represents input file name
:return: support list each item
"""
id1 = 0
self._partialPeriodicPatterns__maxPer = self.__convert(self._partialPeriodicPatterns__maxPer)
self._partialPeriodicPatterns__minSup = self.__convert(self._partialPeriodicPatterns__minSup)
self._partialPeriodicPatterns__minPR = float(self._partialPeriodicPatterns__minPR)
for line in self.__Database:
self.__lno += 1
s = line
n = int(s[0])
self.__last = max(self.__last, n)
for i in range(1, len(s)):
si = s[i]
if abs(0 - n) <= self._partialPeriodicPatterns__maxPer:
if si not in self.__mapSupport:
self.__mapSupport[si] = [1, 1, n]
self.__tidlist[si] = [n]
else:
lp = abs(n - self.__mapSupport[si][2])
if lp <= self._partialPeriodicPatterns__maxPer:
self.__mapSupport[si][0] += 1
self.__mapSupport[si][1] += 1
self.__mapSupport[si][2] = n
self.__tidlist[si].append(n)
else:
if si not in self.__mapSupport:
self.__mapSupport[si] = [0, 1, n]
self.__tidlist[si] = [n]
else:
lp = abs(n - self.__mapSupport[si][2])
if lp <= self._partialPeriodicPatterns__maxPer:
self.__mapSupport[si][0] += 1
self.__mapSupport[si][1] += 1
self.__mapSupport[si][2] = n
self.__tidlist[si].append(n)
for x, y in self.__mapSupport.items():
lp = abs(self.__last - self.__mapSupport[x][2])
if lp <= self._partialPeriodicPatterns__maxPer:
self.__mapSupport[x][0] += 1
self.__mapSupport = {k: [v[1], v[0]] for k, v in self.__mapSupport.items() if
v[1] >= self._partialPeriodicPatterns__minSup and v[0] / (self._partialPeriodicPatterns__minSup + 1) >= self._partialPeriodicPatterns__minPR}
plist = [key for key, value in sorted(self.__mapSupport.items(), key=lambda x: (x[1][0], x[0]), reverse=True)]
return plist
def __save(self, prefix, suffix, tidsetx):
"""
sava prefix patterns with support and periodic ratio
:param prefix: prefix patterns
:type prefix: list
:param suffix: it represents suffix itemsets
:type suffix: list
:param tidsetx: it represents prefix tids
:type tidsetx: list
"""
tidsetx = list(set(tidsetx))
if (prefix == None):
prefix = suffix
else:
prefix = prefix + suffix
val = self._partialPeriodicPatterns__getPerSup(tidsetx)
val1 = self.__getPer_Sup(tidsetx)
if len(tidsetx) >= self._partialPeriodicPatterns__minSup and val / (len(tidsetx) + 1) >= self._partialPeriodicPatterns__minPR:
self._partialPeriodicPatterns__finalPatterns[tuple(prefix)] = [len(tidsetx), val1]
def __Generation(self, prefix, itemsets, tidsets):
"""
here equibalence class is followed amd checks from the patterns generated for periodic frequent patterns.
:param prefix: main equivalence prefix
:type prefix: periodic-frequent item or pattern
:param itemsets: patterns which are items combined with prefix and satisfy the periodicity
and frequent with their time stamps
:type itemsets: list
:param tidsets: time stamps of the items in the argument itemSets
:type tidsets: list
"""
if (len(itemsets) == 1):
i = itemsets[0]
tidi = tidsets[0]
self.__save(prefix, [i], tidi)
return
for i in range(len(itemsets)):
itemx = itemsets[i]
if (itemx == None):
continue
tidsetx = tidsets[i]
classItemsets = []
classtidsets = []
itemsetx = [itemx]
for j in range(i + 1, len(itemsets)):
itemj = itemsets[j]
tidsetj = tidsets[j]
y = list(set(tidsetx) & set(tidsetj))
val = self._partialPeriodicPatterns__getPerSup(y)
if len(y) >= self._partialPeriodicPatterns__minSup and val / (self._partialPeriodicPatterns__minSup + 1) >= self._partialPeriodicPatterns__minPR:
classItemsets.append(itemj)
classtidsets.append(y)
newprefix = list(set(itemsetx)) + prefix
self.__Generation(newprefix, classItemsets, classtidsets)
self.__save(prefix, list(set(itemsetx)), tidsetx)
[docs]
def startMine(self):
"""
Main program start with extracting the periodic frequent items from the database and
performs prefix equivalence to form the combinations and generates closed periodic frequent patterns.
"""
self.__path = self._partialPeriodicPatterns__iFile
self._partialPeriodicPatterns__startTime = time.time()
self.__creatingItemSets()
plist = self.__oneItems(self.__path)
self._partialPeriodicPatterns__finalPatterns = {}
for i in range(len(plist)):
itemx = plist[i]
tidsetx = self.__tidlist[itemx]
itemsetx = [itemx]
itemsets = []
tidsets = []
for j in range(i + 1, len(plist)):
itemj = plist[j]
tidsetj = self.__tidlist[itemj]
y1 = list(set(tidsetx) & set(tidsetj))
val = self._partialPeriodicPatterns__getPerSup(y1)
if len(y1) >= self._partialPeriodicPatterns__minSup and val / (self._partialPeriodicPatterns__minSup + 1) >= self._partialPeriodicPatterns__minPR:
itemsets.append(itemj)
tidsets.append(y1)
self.__Generation(itemsetx, itemsets, tidsets)
self.__save(None, itemsetx, tidsetx)
self._partialPeriodicPatterns__endTime = time.time()
self.__runTime = self._partialPeriodicPatterns__endTime - self._partialPeriodicPatterns__startTime
process = psutil.Process(os.getpid())
self._partialPeriodicPatterns__memoryUSS = float()
self._partialPeriodicPatterns__memoryRSS = float()
self._partialPeriodicPatterns__memoryUSS = process.memory_full_info().uss
self._partialPeriodicPatterns__memoryRSS = process.memory_info().rss
# print("eclat Time taken:",temp)
# print("eclat Memory Space:",resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
[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._partialPeriodicPatterns__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.__runTime
[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._partialPeriodicPatterns__finalPatterns.items():
if len(a) == 1:
pattern = f'{a[0]}'
else:
pattern = f'{a[0]}'
for item in a[1:]:
pattern = pattern + f' {item}'
data.append([pattern, b[0], b[1]])
dataframe = pd.DataFrame(data, columns=['Patterns', 'Support', 'Periodicity'])
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: csv file
"""
self._partialPeriodicPatterns__oFile = outFile
writer = open(self._partialPeriodicPatterns__oFile, 'w+')
for x, y in self._partialPeriodicPatterns__finalPatterns.items():
if len(x) == 1:
writer.write(f'{x[0]}:{y[0]}:{y[1]}\n')
else:
writer.write(f'{x[0]}')
for item in x[1:]:
writer.write(f'\t{item}')
writer.write(f':{y[0]}:{y[1]}\n')
# s1 = str(x) + ":" + 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._partialPeriodicPatterns__finalPatterns
[docs]
def printResults(self):
"""
this function is used to print the results
"""
print("Total number of Partial Periodic 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(sys.argv) == 6 or len(sys.argv) == 7:
if len(sys.argv) == 7:
ap = PPF_DFS(sys.argv[1], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])
if len(sys.argv) == 6:
ap = PPF_DFS(sys.argv[1], sys.argv[3], sys.argv[4], sys.argv[5])
ap.startMine()
print("Total number of Frequent Patterns:", len(ap.getPatterns()))
ap.save(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 [1000, 2000, 3000, 4000, 5000]:
_ap = PPF_DFS('/Users/Likhitha/Downloads/temporal_T10I4D100K.csv', i, 500, 0.7, '\t')
_ap.startMine()
print("Total number of Maximal Partial Periodic Patterns:", len(_ap.getPatterns()))
_ap.save('/Users/Likhitha/Downloads/output.txt')
print("Total Memory in USS:", _ap.getMemoryUSS())
print("Total Memory in RSS", _ap.getMemoryRSS())
print("Total ExecutionTime in ms:", _ap.getRuntime())
print("Error! The number of input parameters do not match the total number of parameters provided")