# VBFTMine is one of the fundamental algorithm to discover fault-tolerant frequent patterns in an uncertain transactional database based on bitset representation.
#
# **Importing this algorithm into a python program**
#
# import PAMI.uncertainFaultTolerantFrequentPattern.basic.VBFTMine as alg
#
# iFile = 'sampleDB.txt'
#
# minSup = 10 # can also be specified between 0 and 1
#
# itemSup = 2 # can also be specified between 0 and 1
#
# minLength = 3 # can also be specified between 0 and 1
#
# faultTolerance = 2 # can also be specified between 0 and 1
#
# obj = alg.VBFTMine(iFile, minSup, itemSup, minLength, faultTolerance)
#
# obj.mine()
#
# faultTolerantFrequentPattern = obj.getPatterns()
#
# print("Total number of Fault Tolerant Frequent Patterns:", len(faultTolerantFrequentPattern))
#
# 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/>.
"""
import pandas as pd
from deprecated import deprecated
import numpy as _np
from PAMI.faultTolerantFrequentPattern.basic import abstract as _ab
[docs]
class VBFTMine(_ab._faultTolerantFrequentPatterns):
"""
About this algorithm
====================
:Description: VBFTMine is one of the fundamental algorithm to discover fault tolerant frequent patterns in an uncertain transactional database based on
bitset representation.
This program employs apriori property (or downward closure property) to reduce the search space effectively.
:Reference: Koh, JL., Yo, PW. (2005). An Efficient Approach for Mining Fault-Tolerant Frequent Patterns Based on Bit Vector Representations.
In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science,
vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_51
:param iFile: str :
Name of the Input file to mine complete set of uncertain Fault Tolerant FrequentFrequent Patterns
:param oFile: str :
Name of the output file to store complete set of uncertain Fault Tolerant FrequentFrequent Patterns
: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 minLength: int
minimum length of a pattern
:param faultTolerance: int :
The ability of a pattern mining algorithm to handle errors or inconsistencies in the data without completely failing or producing incorrect results.
: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:
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
Database : list
To store the transactions of a database in list
Execution methods
=================
**Terminal command**
.. code-block:: console
Format:
(.venv) $ python3 VBFTMine.py <inputFile> <outputFile> <minSup> <itemSup> <minLength> <faultTolerance>
Examples usage:
(.venv) $ python3 VBFTMine.py sampleDB.txt patterns.txt 10.0 3.0 3 1
.. note:: minSup will be considered in times of minSup and count of database transactions
**Calling from a python program**
.. code-block:: python
import PAMI.faultTolerantFrequentPattern.basic.VBFTMine as alg
iFile = 'sampleDB.txt'
minSup = 10 # can also be specified between 0 and 1
itemSup = 2 # can also be specified between 0 and 1
minLength = 3 # can also be specified between 0 and 1
faultTolerance = 2 # can also be specified between 0 and 1
obj = alg.VBFTMine(iFile, minSup, itemSup, minLength, faultTolerance)
obj.mine()
faultTolerantFrequentPattern = obj.getPatterns()
print("Total number of Fault Tolerant Frequent Patterns:", len(faultTolerantFrequentPattern))
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 P.Likhitha under the supervision of Professor Rage Uday Kiran.
"""
_minSup = float()
_itemSup = float()
_minLength = int()
_faultTolerance = int()
_startTime = float()
_endTime = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_plist = []
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_mapSupport = {}
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):
temp = []
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'Transactions' in i:
temp = self._iFile['Transactions'].tolist()
for k in temp:
self._Database.append(set(k))
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(set(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]
for i in temp:
if i not in self._plist:
self._plist.append(i)
self._Database.append(set(temp))
except IOError:
print("File Not Found")
quit()
def _convert(self, value):
"""
To convert the 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 _Count(self, tids):
"""
Count the occurrences of 1s in the given list of transaction IDs.
:param tids: List of transaction IDs.
:type tids: List[int]
:return: Count of occurrences of 1s in the list.
:rtype: int
"""
count = 0
for i in tids:
if i == 1:
count += 1
return count
def _save(self, prefix, suffix, tidsetx):
"""
Save the pattern with its support count if it meets the fault tolerance criteria.
:param prefix: Prefix part of the pattern.
:type prefix: list
:param suffix: Suffix part of the pattern.
:type suffix: list
:param tidsetx: Transaction IDs associated with the pattern.
:type tidsetx: list
:return: None
"""
if prefix == None:
prefix = suffix
else:
prefix = prefix + suffix
prefix = list(set(prefix))
prefix.sort()
val = self._Count(tidsetx)
if len(prefix) > self._faultTolerance:
self._finalPatterns[tuple(prefix)] = val
def _processEquivalenceClass(self, prefix, itemsets, tidsets):
"""
Process the equivalence class to generate frequent patterns.
:param prefix: Prefix part of the pattern.
:type prefix: list.
:param itemsets: List of itemsets in the equivalence class.
:type itemsets: list.
:param tidsets: List of transaction IDs associated with each itemset.
:type tidsets: list
:return: None
"""
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(_np.array(tidsetx) & _np.array(tidsetj))
total = self._Count(y)
if total >= self._minSup:
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)
def _oneLengthFrequentItems(self):
"""
To calculate the one Length items
"""
Vector = {}
items = []
for i in self._Database:
for j in self._plist:
count = 0
if j in i:
count = 1
if j in Vector:
Vector[j].append(count)
else:
Vector[j] = [count]
for x, y in Vector.items():
v = self._Count(y)
if v >= self._itemSup:
items.append(x)
return Vector, items
[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):
"""
Frequent pattern mining process will start from here
"""
self.mine()
[docs]
def mine(self):
"""
Frequent pattern mining process will start from here
"""
self._Database = []
self._startTime = _ab._time.time()
self._creatingItemSets()
self._minSup = self._convert(self._minSup)
self._itemSup = self._convert(self._itemSup)
self._minLength = int(self._minLength)
self._faultTolerance = int(self._faultTolerance)
Vector, plist = self._oneLengthFrequentItems()
for i in range(len(plist)):
itemx = plist[i]
tidsetx = Vector[itemx]
itemsetx = [itemx]
itemsets = []
tidsets = []
for j in range(i + 1, len(plist)):
itemj = plist[j]
tidsetj = Vector[itemj]
y1 = list(_np.array(tidsetx) | _np.array(tidsetj))
total = self._Count(y1)
if total >= self._minSup:
itemsets.append(itemj)
tidsets.append(y1)
if len(itemsets) > 0:
self._processEquivalenceClass(itemsetx, itemsets, tidsets)
self._save(None, 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("Fault-Tolerant Frequent patterns were generated successfully using VBFTMine 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():
s = str()
for i in a:
s = s + i + ' '
data.append([s, b])
dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
# dataFrame = dataFrame.replace(r'\r+|\n+|\t+',' ', regex=True)
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: file
"""
self._oFile = outFile
writer = open(self._oFile, 'w+')
for x, y in self._finalPatterns.items():
s = str()
for i in x:
s = s + i + '\t'
s1 = s.strip() + ":" + 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._finalPatterns
[docs]
def printResults(self):
"""
This function is used to print the results
"""
print("Total number of 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) == 7 or len(_ab._sys.argv) == 8:
if len(_ab._sys.argv) == 8:
_ap = VBFTMine(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4],
_ab._sys.argv[5], _ab._sys.argv[6], _ab._sys.argv[7],)
if len(_ab._sys.argv) == 7:
_ap = VBFTMine(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5], _ab._sys.argv[6])
_ap.mine()
_ap.mine()
print("Total number of 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:
_ap = VBFTMine('/Users/Likhitha/Downloads/fault/sample4.txt', 5, 3, 2, 1, ' ')
_ap.mine()
_ap.printResults()
print(_ap.getPatternsAsDataFrame())
print("Error! The number of input parameters do not match the total number of parameters provided")