# TUFP is one of the fundamental algorithm to discover top-k frequent patterns in a uncertain transactional database using CUP-Lists.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
# from PAMI.uncertainFrequentPattern.basic import TUFP as alg
#
# iFile = 'sampleDB.txt'
#
# minSup = 10 # can also be specified between 0 and 1
#
# obj = alg.TUFP(iFile, minSup)
#
# obj.mine()
#
# frequentPatterns = obj.getPatterns()
#
# print("Total number of Frequent Patterns:", len(frequentPatterns))
#
# obj.save(oFile)
#
# 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/>.
"""
from PAMI.uncertainFrequentPattern.basic import abstract as _ab
from typing import List, Dict, Union
import pandas as pd
from deprecated import deprecated
_minSup = float()
_finalPatterns = {}
class _Item:
"""
A class used to represent the item with probability in transaction of dataset
:Attributes:
item : int or word
Represents the name of the item
probability : float
Represent the existential probability(likelihood presence) of an item
"""
def __init__(self, item, probability) -> None:
self.item = item
self.probability = probability
[docs]
class TUFP(_ab._frequentPatterns):
"""
About this algorithm
====================
:Description: It is one of the fundamental algorithm to discover top-k frequent patterns in a uncertain transactional database using CUP-Lists.
:Reference: Tuong Le, Bay Vo, Van-Nam Huynh, Ngoc Thanh Nguyen, Sung Wook Baik 5, "Mining top-k frequent patterns from uncertain databases",
Springer Science+Business Media, LLC, part of Springer Nature 2020, https://doi.org/10.1007/s10489-019-01622-1
: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 : 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
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
To represent the total no of transaction
tree : class
To represents the Tree class
itemSetCount : int
To represents the total no of patterns
finalPatterns : dict
To store the complete patterns
:Methods:
mine()
Mining process will start from here
getPatterns()
Complete set of patterns will be retrieved with this function
storePatternsInFile(oFile)
Complete set of frequent patterns will be loaded in to a output file
getPatternsInDataFrame()
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
creatingItemSets(fileName)
Scans the dataset and stores in a list format
frequentOneItem()
Extracts the one-length frequent patterns from database
updateTransactions()
Update the transactions by removing non-frequent items and sort the Database by item decreased support
buildTree()
After updating the Database, remaining items will be added into the tree by setting root node as null
convert()
to convert the user specified value
mine()
Mining process will start from this function
Execution methods
=================
**Terminal command**
.. code-block:: console
Format:
(.venv) $ python3 TUFP.py <inputFile> <outputFile> <minSup>
Example Usage:
(.venv) $ python3 TUFP.py sampleDB.txt patterns.txt 0.6
.. note:: minSup can be specified in support count or a value between 0 and 1.
**Calling from a python program**
.. code-block:: python
from PAMI.uncertainFrequentPattern.basic import TUFP as alg
iFile = 'sampleDB.txt'
minSup = 10 # can also be specified between 0 and 1
obj = alg.TUFP(iFile, minSup)
obj.mine()
frequentPatterns = obj.getPatterns()
print("Total number of Frequent Patterns:", len(frequentPatterns))
obj.save(oFile)
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.
"""
_startTime = float()
_endTime = float()
_minSup = str()
_finalPatterns = {}
_iFile = " "
oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_cupList = {}
_topk = {}
_minimum = 9999
def _creatingItemSets(self) -> None:
"""
Scans the dataset
"""
self._Database = []
if isinstance(self._iFile, _ab._pd.DataFrame):
uncertain, data = [], []
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'Transactions' in i:
self._Database = self._iFile['Transactions'].tolist()
if 'uncertain' in i:
uncertain = self._iFile['uncertain'].tolist()
for k in range(len(data)):
tr = []
for j in range(len(data[k])):
product = _Item(data[k][j], uncertain[k][j])
tr.append(product)
self._Database.append(tr)
# print(self.Database)
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]
tr = []
for i in temp:
i1 = i.index('(')
i2 = i.index(')')
item = i[0:i1]
probability = float(i[i1 + 1:i2])
product = _Item(item, probability)
tr.append(product)
self._Database.append(temp)
else:
try:
with open(self._iFile, 'r') as f:
for line in f:
temp = [i.rstrip() for i in line.split(self._sep)]
temp = [x for x in temp if x]
tr = []
for i in temp:
i1 = i.index('(')
i2 = i.index(')')
item = i[0:i1]
probability = float(i[i1 + 1:i2])
product = _Item(item, probability)
tr.append(product)
self._Database.append(tr)
except IOError:
print("File Not Found")
def _frequentOneItem(self) -> List[str]:
"""
Takes the self.Database and calculates the support of each item in the dataset and assign the ranks to the items by decreasing support and returns the frequent items list
:param self.Database : it represents the one self.Database in database
:type self.Database : list
"""
mapSupport = {}
k = 0
for i in self._Database:
k += 1
for j in i:
if j.item not in mapSupport:
mapSupport[j.item] = j.probability
self._cupList[j.item] = {k:j.probability}
else:
mapSupport[j.item] += j.probability
self._cupList[j.item].update({k: j.probability})
plist = [k for k,v in sorted(mapSupport.items(), key=lambda x_: x_[1], reverse=True)]
k = 0
for x, in plist:
k +=1
if k >= self._minSup:
break
self._finalPatterns[x] = mapSupport[x]
self._minimum = min(list(self._finalPatterns.values()))
return plist
@staticmethod
def _convert(value: Union[int, float, str]) -> Union[int, float]:
"""
To convert the type of user specified minSup value
:param value: user specified minSup value
:return: converted type minSup value
"""
if type(value) is int:
value = int(value)
if type(value) is float:
value = float(value)
if type(value) is str:
if '.' in value:
value = float(value)
else:
value = int(value)
return value
def _save(self, prefix: List[str], suffix: List[str], tidSetI: Dict[int, float]) -> None:
"""
Saves the patterns that satisfy the periodic frequent property.
:param prefix: the prefix of a pattern
:type prefix: list
:param suffix: the suffix of a patterns
:type suffix: list
:param tidSetI: the timestamp of a patterns
:type tidSetI: dict
"""
if prefix is None:
prefix = suffix
else:
prefix = prefix + suffix
val = sum(tidSetI.values())
# print(prefix, val)
if len(self._finalPatterns) <= self._minSup:
sample = str()
for i in prefix:
sample = sample + i + " "
self._finalPatterns[sample] = val
if len(self._finalPatterns) == self._minSup:
if val > self._minimum:
sample = str()
for i in prefix:
sample = sample + i + " "
index = list(self._finalPatterns.keys())[list(self._finalPatterns.values()).index(self._minimum)]
del self._finalPatterns[index]
self._finalPatterns[sample] = val
self._minimum = min(list(self._finalPatterns.values()))
# print(self.finalPatterns, self.minimum, self.minSup)
def _Generation(self, prefix: List[str], itemSets: List[str], tidSets: List[Dict[int, float]]) -> None:
"""
Equivalence class is followed and checks for 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 satisfying the periodicity and frequent with their timestamps
:type itemSets: list
:param tidSets: timestamps 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(0, len(itemSets)):
itemI = itemSets[i]
if itemI is None:
continue
tidSetI = tidSets[i]
classItemSets = []
classTidSets = []
itemSetX = [itemI]
for j in range(i + 1, len(itemSets)):
itemJ = itemSets[j]
tidSetJ = tidSets[j]
y = {key: tidSetJ[key] * tidSetI.get(key, 0) for key in tidSetJ.keys()}
#sum2 = sum(list(y.values()))
# print(prefix, itemJ, y, sum2)
# if sum2 >= self.minimum:
self._save(prefix, [itemJ], y)
classItemSets.append(itemJ)
classTidSets.append(y)
# print(itemI, tidSetI, classItemSets)
newPrefix = list(set(itemSetX)) + prefix
self._Generation(newPrefix, classItemSets, classTidSets)
# self.save(prefix, list(set(itemSetX)), tidSetI)
[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) -> None:
"""
Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns
"""
self.mine()
[docs]
def mine(self) -> None:
"""
Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns
"""
global _minSup
self._startTime = _ab._time.time()
self._creatingItemSets()
self._minSup = self._convert(self._minSup)
_minSup = self._minSup
plist = self._frequentOneItem()
for i in range(len(plist)):
itemI = plist[i]
tidSetI = self._cupList[itemI]
itemSetX = [itemI]
itemSets = []
tidSets = []
for j in range(i + 1, len(plist)):
itemJ = plist[j]
tidSetJ = self._cupList[itemJ]
y1 = {key: tidSetJ[key] * tidSetI.get(key, 0) for key in tidSetJ.keys()}
self._save(itemSetX, [itemJ], y1)
itemSets.append(itemJ)
tidSets.append(y1)
self._Generation(itemSetX, itemSets, tidSets)
print("Top-K Frequent patterns were generated from uncertain databases successfully using TUFP algorithm")
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
[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._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._endTime - self._startTime
[docs]
def getPatternsAsDataFrame(self) -> pd.DataFrame:
"""
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])
dataframe = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
return dataframe
[docs]
def save(self, outFile: str) -> None:
"""
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():
s1 = x + ":" + str(y)
writer.write("%s \n" % s1)
[docs]
def getPatterns(self) -> Dict[str, float]:
"""
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) -> None:
"""
This function is used to print the results
"""
print("Total number of Uncertain 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) == 4 or len(_ab._sys.argv) == 5:
if len(_ab._sys.argv) == 5:
_ap = TUFP(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
if len(_ab._sys.argv) == 4:
_ap = TUFP(_ab._sys.argv[1], _ab._sys.argv[3])
_ap.mine()
_ap.mine()
_Patterns = _ap.getPatterns()
print("Total number of Patterns:", len(_Patterns))
_ap.save(_ab._sys.argv[2])
_memUSS = _ap.getMemoryUSS()
print("Total Memory in USS:", _memUSS)
_memRSS = _ap.getMemoryRSS()
print("Total Memory in RSS", _memRSS)
_run = _ap.getRuntime()
print("Total ExecutionTime in ms:", _run)
else:
'''ap = TUFP("/home/apiiit-rkv/Desktop/uncertain/tubeSample", 10, ' ')
ap.mine()
Patterns = ap.getPatterns()
print("Total number of Patterns:", len(Patterns))
ap.save("patterns.txt")
memUSS = ap.getMemoryUSS()
print("Total Memory in USS:", memUSS)
memRSS = ap.getMemoryRSS()
print("Total Memory in RSS", memRSS)
run = ap.getRuntime()
print("Total ExecutionTime in ms:", run)'''
print("Error! The number of input parameters do not match the total number of parameters provided")