# CPFPMiner algorithm is used to discover the closed periodic frequent patterns in temporal databases.
# It uses depth-first search.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
#
# from PAMI.periodicFrequentPattern.closed import CPFPMiner as alg
#
# obj = alg.CPFPMiner("../basic/sampleTDB.txt", "2", "6")
#
# obj.mine()
#
# periodicFrequentPatterns = obj.getPatterns()
#
# print("Total number of Frequent Patterns:", len(periodicFrequentPatterns))
#
# obj.save("patterns")
#
# 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.periodicFrequentPattern.basic import abstract as _ab
import pandas as pd
from deprecated import deprecated
from PAMI.periodicFrequentPattern.closed import abstract as _ab
[docs]
class CPFPMiner(_ab._periodicFrequentPatterns):
"""
About this algorithm
====================
:Description: CPFPMiner algorithm is used to discover the closed periodic frequent patterns in temporal databases.
It uses depth-first search.
:Reference: P. Likhitha et al., "Discovering Closed Periodic-Frequent Patterns in Very Large Temporal Databases"
2020 IEEE International Conference on Big Data (Big Data), 2020, https://ieeexplore.ieee.org/document/9378215
:param iFile: str :
Name of the Input file to mine complete set of periodic frequent pattern's
:param oFile: str :
Name of the output file to store complete set of periodic frequent pattern's
:param minSup: float:
Controls the minimum number of transactions in which every item must appear in a database.
:param maxPer: float:
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 : str
Input file name or path of the input file
oFile : str
Name of the output file or path of the input file
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.
Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
maxPer: int or float or str
The user can specify maxPer either 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.
Otherwise, it will be treated as float.
Example: maxPer=10 will be treated as integer, while maxPer=10.0 will be treated as float
sep : str
This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or \t.
However, the users can override their default separator.
startTime:float
To record the start time of the mining process
endTime:float
To record the completion time of the mining process
finalPatterns: dict
Storing the complete set of patterns in a dictionary variable
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
: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 an 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
Execution methods
=================
**Terminal command**
.. code-block:: console
Format:
(.venv) $ python3 CPFPMiner.py <inputFile> <outputFile> <minSup> <maxPer>
Example:
(.venv) $ python3 CPFPMiner.py sampleTDB.txt patterns.txt 0.3 0.4
.. note:: minSup will be considered in percentage of database transactions
**Calling from a python program**
.. code-block:: python
from PAMI.periodicFrequentPattern.closed import CPFPMiner as alg
obj = alg.CPFPMiner("../basic/sampleTDB.txt", "2", "6")
obj.mine()
periodicFrequentPatterns = obj.getPatterns()
print("Total number of Frequent Patterns:", len(periodicFrequentPatterns))
obj.save("patterns")
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 P.Likhitha under the supervision of Professor Rage Uday Kiran.
"""
_minSup = float()
_maxPer = float()
_startTime = float()
_endTime = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_transaction = []
_hashing = {}
_mapSupport = {}
_itemSetCount = 0
_maxItemId = 0
_tableSize = 10000
_tidList = {}
_lno = 0
def __init__(self, iFile, minSup, maxPer, sep='\t'):
super().__init__(iFile, minSup, maxPer, sep)
self._finalPatterns = {}
def _convert(self, value):
"""
To convert the given user specified value
:param value: user specified value
:return: converted value
"""
if type(value) is int:
value = int(value)
if type(value) is float:
value = (self._lno * value)
if type(value) is str:
if '.' in value:
value = float(value)
value = (self._lno * value)
else:
value = int(value)
return value
def _scanDatabase(self):
"""
To scan the database and extracts the 1-length periodic-frequent items
:return: Returns the 1-length periodic-frequent items
"""
Database = []
if isinstance(self._iFile, _ab._pd.DataFrame):
ts, data = [], []
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'TS' in i:
ts = self._iFile['TS'].tolist()
if 'Transactions' in i:
data = self._iFile['Transactions'].tolist()
for i in range(len(data)):
tr = [ts[i][0]]
tr = tr + data[i]
Database.append(tr)
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]
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]
Database.append(temp)
except IOError:
print("File Not Found")
quit()
self._tidList = {}
self._mapSupport = {}
for line in Database:
self._lno += 1
s = line
n = int(s[0])
for i in range(1, len(s)):
si = s[i]
if self._mapSupport.get(si) is None:
self._mapSupport[si] = [1, abs(0 - n), n]
self._tidList[si] = [n]
else:
self._mapSupport[si][0] += 1
self._mapSupport[si][1] = max(self._mapSupport[si][1], abs(n - self._mapSupport[si][2]))
self._mapSupport[si][2] = n
self._tidList[si].append(n)
for x, y in self._mapSupport.items():
self._mapSupport[x][1] = max(self._mapSupport[x][1], abs(self._lno - self._mapSupport[x][2]))
self._minSup = self._convert(self._minSup)
self._maxPer = self._convert(self._maxPer)
self._mapSupport = {k: [v[0], v[1]] for k, v in self._mapSupport.items() if
v[0] >= self._minSup and v[1] <= self._maxPer}
periodicFrequentItems = {}
self._tidList = {k: v for k, v in self._tidList.items() if k in self._mapSupport}
for x, y in self._tidList.items():
t1 = 0
for i in y:
t1 += i
periodicFrequentItems[x] = t1
periodicFrequentItems = [key for key, value in sorted(periodicFrequentItems.items(), key=lambda x: x[1])]
return periodicFrequentItems
def _calculate(self, tidSet):
"""
To calculate the weight if pattern based on the respective timeStamps
:param tidSet: timeStamps of the pattern
:return: the calculated weight of the timeStamps
"""
hashcode = 0
for i in tidSet:
hashcode += i
if hashcode < 0:
hashcode = abs(0 - hashcode)
return hashcode % self._tableSize
def _contains(self, itemSet, val, hashcode):
"""
To check if the key(hashcode) is in dictionary(hashing) variable
:param itemSet: generated periodic-frequent itemSet
:param val: support and periodicity of itemSet
:param hashcode: the key generated in calculate() method for every itemSet
:return: true if itemSet with same support present in dictionary(hashing) or else returns false
"""
if self._hashing.get(hashcode) is None:
return False
for i in self._hashing[hashcode]:
itemSetX = i
if val[0] == self._hashing[hashcode][itemSetX][0] and set(itemSetX).issuperset(itemSet):
return True
return False
def _getPeriodAndSupport(self, timeStamps):
"""
Calculates the periodicity and support of timeStamps
:param timeStamps: timeStamps of itemSet
:return: periodicity and support
"""
timeStamps.sort()
cur = 0
per = 0
sup = 0
for j in range(len(timeStamps)):
per = max(per, timeStamps[j] - cur)
if per > self._maxPer:
return [0, 0]
cur = timeStamps[j]
sup += 1
per = max(per, self._lno - cur)
return [sup, per]
def _save(self, prefix, suffix, tidSetX):
"""
Saves the generated pattern which satisfies the closed property
Parameters:
-----------
prefix: the prefix part of itemSet
suffix: the suffix part of itemSet
tidSetX: the timeStamps of the generated itemSet
Returns:
--------
saves the closed periodic-frequent pattern
"""
if prefix is None:
prefix = suffix
else:
prefix = prefix + suffix
prefix = list(set(prefix))
prefix.sort()
val = self._getPeriodAndSupport(tidSetX)
if val[0] >= self._minSup and val[1] <= self._maxPer:
hashcode = self._calculate(tidSetX)
if self._contains(prefix, val, hashcode) is False:
self._itemSetCount += 1
sample = str()
for i in prefix:
sample = sample + i + " "
self._finalPatterns[sample] = val
if hashcode not in self._hashing:
self._hashing[hashcode] = {tuple(prefix): val}
else:
self._hashing[hashcode][tuple(prefix)] = val
def _processEquivalenceClass(self, prefix, itemSets, tidSets):
"""
identifies and saves closed periodic patterns of length more than 2 in a dataset, by processing equivalence classes of item sets that satisfy a minimum support condition.
:param prefix: Prefix class of an itemSet
:param itemSets: suffix items in periodicFrequentItems that satisfies the minSup condition
:param tidSets: timeStamps of items in itemSets respectively
:return: closed periodic patterns with length more than 2
"""
if len(itemSets) == 1:
i = itemSets[0]
tidList = tidSets[0]
self._save(prefix, [i], tidList)
return
if len(itemSets) == 2:
itemI = itemSets[0]
tidSetI = tidSets[0]
itemJ = itemSets[1]
tidSetJ = tidSets[1]
y1 = list(set(tidSetI).intersection(tidSetJ))
if len(y1) >= self._minSup:
suffix = []
suffix += [itemI, itemJ]
suffix = list(set(suffix))
self._save(prefix, suffix, y1)
if len(y1) != len(tidSetI):
self._save(prefix, [itemI], tidSetI)
if len(y1) != len(tidSetJ):
self._save(prefix, [itemJ], tidSetJ)
return
for i in range(len(itemSets)):
itemX = itemSets[i]
if itemX is None:
continue
tidSetX = tidSets[i]
classItemSets = []
classTidSets = []
itemSetX = [itemX]
for j in range(i + 1, len(itemSets)):
itemJ = itemSets[j]
if itemJ is None:
continue
tidSetJ = tidSets[j]
y = list(set(tidSetX).intersection(tidSetJ))
if len(y) < self._minSup:
continue
if len(tidSetX) == len(tidSetJ) and len(y) == len(tidSetX):
itemSets.insert(j, None)
tidSets.insert(j, None)
itemSetX.append(itemJ)
elif len(tidSetJ) > len(tidSetX) == len(y):
itemSetX.append(itemJ)
elif len(tidSetX) > len(tidSetJ) and len(y) == len(tidSetJ):
itemSets.insert(j, None)
tidSets.insert(j, None)
classItemSets.append(itemJ)
classTidSets.append(y)
else:
classItemSets.append(itemJ)
classTidSets.append(y)
if len(classItemSets) > 0:
newPrefix = list(set(itemSetX)) + prefix
self._processEquivalenceClass(newPrefix, classItemSets, classTidSets)
self._save(prefix, list(set(itemSetX)), tidSetX)
[docs]
@deprecated("It is recommended to use mine() instead of mine() for mining process")
def startMine(self):
"""
Mining process will start from here
"""
self._startTime = _ab._time.time()
self._finalPatterns = {}
self._hashing = {}
periodicFrequentItems = self._scanDatabase()
for i in range(len(periodicFrequentItems)):
itemX = periodicFrequentItems[i]
if itemX is None:
continue
tidSetX = self._tidList[itemX]
itemSetX = [itemX]
itemSets = []
tidSets = []
for j in range(i + 1, len(periodicFrequentItems)):
itemJ = periodicFrequentItems[j]
if itemJ is None:
continue
tidSetJ = self._tidList[itemJ]
y1 = list(set(tidSetX).intersection(tidSetJ))
if len(y1) < self._minSup:
continue
if len(tidSetX) == len(tidSetJ) and len(y1) is len(tidSetX):
periodicFrequentItems.insert(j, None)
itemSetX.append(itemJ)
elif len(tidSetX) < len(tidSetJ) and len(y1) is len(tidSetX):
itemSetX.append(itemJ)
elif len(tidSetX) > len(tidSetJ) and len(y1) is len(tidSetJ):
periodicFrequentItems.insert(j, None)
itemSets.append(itemJ)
tidSets.append(y1)
else:
itemSets.append(itemJ)
tidSets.append(y1)
if len(itemSets) > 0:
self._processEquivalenceClass(itemSetX, itemSets, tidSets)
self._save([], itemSetX, tidSetX)
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("Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm ")
[docs]
def mine(self):
"""
Mining process will start from here
"""
self._startTime = _ab._time.time()
self._finalPatterns = {}
self._hashing = {}
periodicFrequentItems = self._scanDatabase()
for i in range(len(periodicFrequentItems)):
itemX = periodicFrequentItems[i]
if itemX is None:
continue
tidSetX = self._tidList[itemX]
itemSetX = [itemX]
itemSets = []
tidSets = []
for j in range(i + 1, len(periodicFrequentItems)):
itemJ = periodicFrequentItems[j]
if itemJ is None:
continue
tidSetJ = self._tidList[itemJ]
y1 = list(set(tidSetX).intersection(tidSetJ))
if len(y1) < self._minSup:
continue
if len(tidSetX) == len(tidSetJ) and len(y1) is len(tidSetX):
periodicFrequentItems.insert(j, None)
itemSetX.append(itemJ)
elif len(tidSetX) < len(tidSetJ) and len(y1) is len(tidSetX):
itemSetX.append(itemJ)
elif len(tidSetX) > len(tidSetJ) and len(y1) is len(tidSetJ):
periodicFrequentItems.insert(j, None)
itemSets.append(itemJ)
tidSets.append(y1)
else:
itemSets.append(itemJ)
tidSets.append(y1)
if len(itemSets) > 0:
self._processEquivalenceClass(itemSetX, itemSets, tidSets)
self._save([], itemSetX, tidSetX)
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("Closed periodic frequent patterns were generated successfully using CPFPMiner algorithm ")
[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._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._endTime - self._startTime
[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._finalPatterns.items():
data.append([a, b[0], b[1]])
dataFrame = _ab._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._oFile = outFile
writer = open(self._oFile, 'w+')
for x, y in self._finalPatterns.items():
s1 = x.replace(' ', '\t').strip() + ":" + str(y[0]) + ":" + str(y[1])
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._finalPatterns
[docs]
def printResults(self):
"""
This function is used to print the results
"""
print("Total number of Closed 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(_ab._sys.argv) == 5 or len(_ab._sys.argv) == 6:
if len(_ab._sys.argv) == 6:
_ap = CPFPMiner(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5])
if len(_ab._sys.argv) == 5:
_ap = CPFPMiner(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
_ap.mine()
print("Total number of Closed Periodic-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")