# Stable periodic pattern mining aims to discover all interesting patterns in a temporal database using three constraints minimum support,
# maximum period and maximum liability, that have support no less than the user-specified minimum support constraint and liability no
# greater than maximum liability.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
#
# from PAMI.stablePeriodicFrequentPattern.basic import basic as alg
#
# obj = alg.SPPEclat("../basic/sampleTDB.txt", 5, 3, 3)
#
# obj.mine()
#
# Patterns = obj.getPatterns()
#
# print("Total number of Stable Periodic Frequent Patterns:", len(Patterns))
#
# 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
"""
import pandas as pd
from deprecated import deprecated
from PAMI.stablePeriodicFrequentPattern.basic import abstract as _ab
[docs]
class SPPEclat(_ab._stablePeriodicFrequentPatterns):
"""
:Description: Stable periodic pattern mining aims to dicover all interesting patterns in a temporal database using three contraints minimum support,
maximum period and maximum lability, that have support no less than the user-specified minimum support constraint and lability no
greater than maximum lability.
:Reference: Fournier-Viger, P., Yang, P., Lin, J. C.-W., Kiran, U. (2019). Discovering Stable Periodic-Frequent Patterns in Transactional Data. Proc.
32nd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2019), Springer LNAI, pp. 230-244
:param iFile: str :
Name of the Input file to mine complete set of stable periodic Frequent Pattern.
:param oFile: str :
Name of the output file to store complete set of stable periodic Frequent Pattern.
:param minSup: float or int 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
:param itemSup: int or float :
Frequency of an item
:param maxLa: float :
minimum loss of a pattern
: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 or path of the input file
oFile : file
Name of the output file or path of the output 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
maxLa : int or float or str
The user can specify maxLa either in count or proportion of database size.
If the program detects the data type of maxLa is integer, then it treats maxLa is expressed in count.
Otherwise, it will be treated as float.
Example: maxLa=10 will be treated as integer, while maxLa=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.
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
startTime:float
To record the start time of the mining process
endTime:float
To record the completion time of the mining process
Database : list
To store the transactions of a database in list
mapSupport : Dictionary
To maintain the information of item and their frequency
lno : int
it represents the total no of transactions
tree : class
it represents the Tree class
itemSetCount : int
it represents the total no of patterns
finalPatterns : dict
it represents to store the patterns
tidList : dict
stores the timestamps of an 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 periodic-frequent patterns will be loaded in to an output file
getPatternsAsDataFrame()
Complete set of periodic-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
creatingItemSets()
Scan the database and store the items with their timestamps which are periodic frequent
calculateLa()
Calculates the support and period for a list of timestamps.
Generation()
Used to implement prefix class equivalence method to generate the periodic patterns recursively
**Methods to execute code on terminal**
-----------------------------------------
.. code-block:: console
Format:
(.venv) $ python3 basic.py <inputFile> <outputFile> <minSup> <maxPer> <maxLa>
Example usage:
(.venv) $ python3 basic.py sampleDB.txt patterns.txt 10.0 4.0 2.0
.. note:: constraints will be considered in percentage of database transactions
**Importing this algorithm into a python program**
---------------------------------------------------
... code-block:: python
from PAMI.stablePeriodicFrequentPattern.basic import basic as alg
obj = alg.PFPECLAT("../basic/sampleTDB.txt", 5, 3, 3)
obj.mine()
Patterns = obj.getPatterns()
print("Total number of Stable Periodic Frequent Patterns:", len(Patterns))
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.
"""
_iFile = " "
_oFile = " "
_minSup = str()
_maxPer = str()
_maxLa = float()
_sep = " "
_SPPList = {}
_itemList = []
_last = int()
_finalPatterns = {}
_tsList = {}
_startTime = float()
_endTime = float()
_memoryUSS = float()
_memoryRSS = float()
_Database = []
def __init__(self, iFile, minSup, maxPer,maxLA, sep='\t'):
super().__init__(iFile, minSup, maxPer, maxLA, sep)
self._iFile = iFile
self._minSup = minSup
self._maxPer = maxPer
self._maxLa = maxLA
self._sep = sep
def _creatingItemsets(self):
"""
Storing the complete transactions of the database/input file in a database variable
"""
self._Database = []
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._Database = self._iFile['Transactions'].tolist()
if 'Patterns' in i:
self._Database = self._iFile['Patterns'].tolist()
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]
self._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]
self._Database.append(temp)
except IOError:
print("File Not Found")
quit()
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 _createSPPList(self):
"""
to convert the single length stable periodic patterns
"""
tidLast = {}
la = {}
self._SPPList = {}
self._tsList = {}
for transaction in self._Database:
ts = int(transaction[0])
for item in transaction[1:]:
if item not in self._SPPList:
la[item] = max(0, ts - self._maxPer)
self._SPPList[item] = [1, la[item]]
self._tsList[item] = [ts]
else:
s = self._SPPList[item][0] + 1
la[item] = max(0, la[item] + ts - tidLast.get(item) - self._maxPer)
self._SPPList[item] = [s, max(la[item], self._SPPList[item][1])]
self._tsList[item].append(ts)
tidLast[item] = ts
self._last = ts
for item in self._SPPList:
la[item] = max(0, la[item] + self._last - tidLast[item] - self._maxPer)
self._SPPList[item][1] = max(la[item], self._SPPList[item][1])
self._SPPList = {k: v for k, v in self._SPPList.items() if v[0] >= self._minSup and v[1] <= self._maxLa}
self._SPPList = {k: v for k, v in sorted(self._SPPList.items(), key=lambda x: x[1][0], reverse=True)}
self._Generation(list(self._SPPList), set())
def _Generation(self, GPPFList, CP):
"""
To generate the patterns using depth-first search
"""
for i in range(len(GPPFList)):
item = GPPFList[i]
CP1 = CP | {item}
if CP != set():
self._tsList['\t'.join(CP1)] = list(set(self._tsList['\t'.join(CP)]) & set(self._tsList[item]))
la = self._calculateLa(self._tsList['\t'.join(CP1)])
support = len(self._tsList['\t'.join(CP1)])
if la <= self._maxLa and len(self._tsList['\t'.join(CP1)]) >= self._minSup:
#CP = CP1
self._finalPatterns['\t'.join(CP1)] = [support, la]
if i+1 < len(GPPFList):
self._Generation(GPPFList[i+1:], CP1)
def _calculateLa(self, tsList):
"""
To calculate the liability of a patterns based on its timestamps
"""
previous = 0
la = 0
tsList = sorted(tsList)
laList = []
for ts in tsList:
la = max(0, la + ts - previous - self._maxPer)
laList.append(la)
previous = ts
la = max(0, la + self._last - previous - self._maxPer)
laList.append(la)
maxla = max(laList)
return maxla
[docs]
@deprecated("It is recommended to use mine() instead of mine() for mining process")
def startMine(self):
"""
Method to start the mining of patterns
"""
self.mine()
[docs]
def mine(self):
"""
Method to start the mining of patterns
"""
self._startTime = _ab._time.time()
self._creatingItemsets()
self._minSup = self._convert(self._minSup)
self._maxPer = self._convert(self._maxPer)
self._maxLa = self._convert(self._maxLa)
self._finalPatterns = {}
#print(self._minSup, self._maxPer, self._maxLa)
self._createSPPList()
self._endTime = _ab._time.time()
self._memoryUSS = float()
self._memoryRSS = float()
process = _ab._psutil.Process(_ab._os.getpid())
self._memoryUSS = process.memory_full_info().uss
self._memoryRSS = process.memory_info().rss
print("Stable Periodic Frequent patterns were generated successfully using basic algorithm ")
[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 getPatterns(self):
"""
Function to return the set of stable periodic-frequent patterns after completion of the mining process
:return: returning stable periodic-frequent patterns
:rtype: dict
"""
return self._finalPatterns
[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 save(self, outFile):
"""
Complete set of periodic-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.strip() + ":" + str(y[0]) + ":" + str(y[1])
writer.write("%s \n" % s1)
[docs]
def getPatternsAsDataFrame(self):
"""
Storing final periodic-frequent patterns in a dataframe
:return: returning periodic-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', 'Periodicity'])
return dataFrame
[docs]
def printResults(self):
"""
This function is used to print the results
"""
print("Total number of Stable 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) == 6 or len(_ab._sys.argv) == 7:
if len(_ab._sys.argv) == 7:
_ap = SPPEclat(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5], _ab._sys.argv[6])
if len(_ab._sys.argv) == 6:
_ap = SPPEclat(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5])
_ap.mine()
_ap.mine()
print("Total number of 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")