# WFMiner is one of the fundamental algorithm to discover weighted frequent patterns in a transactional database.
# It stores the database in compressed fp-tree decreasing the memory usage and extracts the
# patterns from tree.It employs downward closure property to reduce the search space effectively.
#
# **Importing this algorithm into a python program**
#
# from PAMI.weightFrequentPattern.basic import basic as alg
#
# iFile = 'sampleDB.txt'
#
# minSup = 10 # can also be specified between 0 and 1
#
# obj = alg.basic(iFile, wFile, minSup, minWeight)
#
# obj.mine()
#
# frequentPatterns = obj.getPatterns()
#
# print("Total number of Frequent Patterns:", len(frequentPatterns))
#
# obj.savePatterns(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.weightedFrequentPattern.basic import abstract as _fp
from typing import List, Dict, Tuple, Union, Generator
import pandas as pd
from deprecated import deprecated
_minSup = str()
_minWeight = int()
_miniWeight = int()
_maxWeight = int()
_weights = {}
_fp._sys.setrecursionlimit(20000)
class _Node:
"""
A class used to represent the node of frequentPatternTree
:Attributes:
itemId: int
storing item of a node
counter: int
To maintain the support of node
parent: node
To maintain the parent of node
children: list
To maintain the children of node
:Methods:
addChild(node)
Updates the nodes children list and parent for the given node
"""
def __init__(self, item: str, children: list) -> None:
self.itemId = item
self.counter = 1
self.parent = None
self.children = children
def addChild(self, node: '_Node') -> None:
"""
Retrieving the child from the tree
:param node: Children node
:type node: _Node
:return: Updates the children nodes and parent nodes
"""
self.children[node.itemId] = node
node.parent = self
class _Tree:
"""
A class used to represent the frequentPatternGrowth tree structure
:Attributes:
root : Node
The first node of the tree set to Null.
summaries : dictionary
Stores the nodes itemId which shares same itemId
info : dictionary
frequency of items in the transactions
:Methods:
addTransaction(transaction, freq)
adding items of transactions into the tree as nodes and freq is the count of nodes
getFinalConditionalPatterns(node)
getting the conditional patterns from fp-tree for a node
getConditionalPatterns(patterns, frequencies)
sort the patterns by removing the items with lower minSup
generatePatterns(prefix)
generating the patterns from fp-tree
"""
def __init__(self) -> None:
self.root = _Node(None, {})
self.summaries = {}
self.info = {}
def addTransaction(self, transaction: List[str], count: int) -> None:
"""
Adding transaction into tree
:param transaction: it represents the one transaction in database
:type transaction: list
:param count: frequency of item
:type count: int
:return: None
"""
# This method takes transaction as input and returns the tree
currentNode = self.root
for i in range(len(transaction)):
if transaction[i] not in currentNode.children:
newNode = _Node(transaction[i], {})
newNode.freq = count
currentNode.addChild(newNode)
if transaction[i] in self.summaries:
self.summaries[transaction[i]].append(newNode)
else:
self.summaries[transaction[i]] = [newNode]
currentNode = newNode
else:
currentNode = currentNode.children[transaction[i]]
currentNode.freq += count
def getFinalConditionalPatterns(self, alpha: str) -> Tuple[List[List[str]], List[int], Dict[str, int]]:
"""
Generates the conditional patterns for a node
:param alpha: node to generate conditional patterns
:return: returns conditional patterns, frequency of each item in conditional patterns
"""
finalPatterns = []
finalFreq = []
for i in self.summaries[alpha]:
set1 = i.freq
set2 = []
while i.parent.itemId is not None:
set2.append(i.parent.itemId)
i = i.parent
if len(set2) > 0:
set2.reverse()
finalPatterns.append(set2)
finalFreq.append(set1)
finalPatterns, finalFreq, info = self.getConditionalTransactions(finalPatterns, finalFreq)
return finalPatterns, finalFreq, info
@staticmethod
def getConditionalTransactions(ConditionalPatterns: List[List[str]], conditionalFreq: List[int]) -> Tuple[List[List[str]], List[int], Dict[str, int]]:
"""
To calculate the frequency of items in conditional patterns and sorting the patterns
:param ConditionalPatterns: paths of a node
:param conditionalFreq: frequency of each item in the path
:return: conditional patterns and frequency of each item in transactions
"""
global _minSup, _miniWeight
pat = []
freq = []
data1 = {}
for i in range(len(ConditionalPatterns)):
for j in ConditionalPatterns[i]:
if j in data1:
data1[j] += conditionalFreq[i]
else:
data1[j] = conditionalFreq[i]
up_dict = {k: v for k, v in data1.items() if v >= _minSup and v * _miniWeight > _minSup}
count = 0
for p in ConditionalPatterns:
p1 = [v for v in p if v in up_dict]
trans = sorted(p1, key=lambda x: (up_dict.get(x), -x), reverse=True)
if len(trans) > 0:
pat.append(trans)
freq.append(conditionalFreq[count])
count += 1
return pat, freq, up_dict
def generatePatterns(self, prefix: List[str]) -> Generator[Tuple[List[str], int], None, None]:
"""
To generate the frequent patterns
:param prefix: an empty list
:return: Frequent patterns that are extracted from fp-tree
"""
global _miniWeight, _maxWeight, _minWeight, _minSup
for i in sorted(self.summaries, key=lambda x: (self.info.get(x), -x)):
pattern = prefix[:]
pattern.append(i)
yield pattern, self.info[i]
patterns, freq, info = self.getFinalConditionalPatterns(i)
conditionalTree = _Tree()
conditionalTree.info = info.copy()
for pat in range(len(patterns)):
conditionalTree.addTransaction(patterns[pat], freq[pat])
if len(patterns) > 0:
for q in conditionalTree.generatePatterns(pattern):
yield q
[docs]
class WFIM(_fp._weightedFrequentPatterns):
"""
About this algorithm
====================
:Description: * WFMiner is one of the fundamental algorithm to discover weighted frequent patterns in a transactional database.
* It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs employs downward closure property to reduce the search space effectively.
:Reference: U. Yun and J. J. Leggett, “Wfim: weighted frequent itemset mining with a weight range and a minimum weight,”
In: Proceedings of the 2005 SIAM International Conference on Data Mining. SIAM, 2005, pp. 636–640.
https://epubs.siam.org/doi/pdf/10.1137/1.9781611972757.76
:param iFile: str :
Name of the Input file to mine complete set of weighted Frequent Patterns.
:param oFile: str :
Name of the output file to store complete set of weighted Frequent Patterns.
:param minSup: str or int or float:
minimum support thresholds were tuned to find the appropriate ranges in the limited memory
: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 name or path of the input 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
minWeight: float or int or str
The user can specify minWeight either in count or proportion of database size.
If the program detects the data type of minWeight is integer, then it treats minWeight is expressed in count.
Otherwise, it will be treated as float.
Example: minWeight=10 will be treated as integer, while minWeight=10.0 will be treated as float
sep : str
This variable is used to distinguish items from one another in a transaction. The default separator is tab space or \t.
However, the users can override their default separator.
oFile : file
Name of the output file or the path of the output file
startTime:float
To record the start time of the mining process
endTime:float
To record the completion time of the mining process
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
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
finalPatterns : dict
it represents to store the patterns
: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 a 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
creatingItemSets()
Scans the dataset or dataframes and stores in list format
frequentOneItem()
Extracts the one-frequent patterns from transactions
Execution methods
=================
**Terminal command**
.. code-block:: console
Format:
(.venv) $ python3 basic.py <inputFile> <weightFile> <outputFile> <minSup> <minWeight>
Example Usage:
(.venv) $ python3 basic.py sampleDB.txt weightSample.txt patterns.txt 10.0 3.4
.. note:: minSup and maxPer will be considered in support count or frequency
**Calling from a python program**
.. code-block:: python
from PAMI.weightFrequentPattern.basic import basic as alg
iFile = 'sampleDB.txt'
minSup = 10 # can also be specified between 0 and 1
obj = alg.basic(iFile, wFile, minSup, minWeight)
obj.mine()
frequentPatterns = obj.getPatterns()
print("Total number of Frequent Patterns:", len(frequentPatterns))
obj.savePatterns(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 = []
__mapSupport = {}
__lno = 0
__tree = _Tree()
__rank = {}
__rankDup = {}
def __init__(self, iFile: str, wFile: str, minSup: str, minWeight: int, sep: str='\t') -> None:
super().__init__(iFile, wFile, minSup, minWeight, sep)
def __creatingItemSets(self) -> None:
"""
Storing the complete transactions of the database/input file in a database variable
:return: None
"""
self.__Database = []
if isinstance(self._iFile, _fp._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()
# print(self.Database)
if isinstance(self._iFile, str):
if _fp._validators.url(self._iFile):
data = _fp._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]
# print(len(temp))
self.__Database.append(temp)
except IOError:
print("File Not Found")
quit()
def _scanningWeights(self) -> None:
"""
Storing the weights of the variables in input file in a weights variable
:return: None
"""
global _weights
_weights = {}
if isinstance(self._wFile, _fp._pd.DataFrame):
items, weights = [], []
if self._wFile.empty:
print("its empty..")
i = self._wFile.columns.values.tolist()
if 'items' in i:
items = self._wFile['items'].tolist()
if 'weights' in i:
weights = self._wFile['weights'].tolist()
for i in range(len(weights)):
_weights[items[i]] = weights[i]
# print(self.Database)
if isinstance(self._wFile, str):
if _fp._validators.url(self._wFile):
data = _fp._urlopen(self._wFile)
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]
_weights[temp[0]] = temp[1]
else:
try:
with open(self._wFile, '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]
s = int(float(temp[1]))
_weights[temp[0]] = s
except IOError:
print("File Not Found")
quit()
def __convert(self, 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
"""
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 __frequentOneItem(self) -> List[str]:
"""
Generating One frequent items sets
:return: list
"""
global _maxWeight
self.__mapSupport = {}
for tr in self.__Database:
for i in range(0, len(tr)):
if tr[i] not in self.__mapSupport:
self.__mapSupport[tr[i]] = 1
else:
self.__mapSupport[tr[i]] += 1
self.__mapSupport = {k: v for k, v in self.__mapSupport.items() if v >= self._minSup and v * _maxWeight > self._minSup}
genList = [k for k, v in sorted(self.__mapSupport.items(), key=lambda x: x[1], reverse=True)]
self.__rank = dict([(index, item) for (item, index) in enumerate(genList)])
return genList
def __updateTransactions(self, itemSet: List[str]) -> List[List[int]]:
"""
Updates the items in transactions with rank of items according to their support
:Example: oneLength = {'a':7, 'b': 5, 'c':'4', 'd':3}
rank = {'a':0, 'b':1, 'c':2, 'd':3}
:param itemSet: list of one-frequent items
:return: list
"""
list1 = []
for tr in self.__Database:
list2 = []
for i in range(len(tr)):
if tr[i] in itemSet:
list2.append(self.__rank[tr[i]])
if len(list2) >= 1:
list2.sort()
list1.append(list2)
return list1
@staticmethod
def __buildTree(transactions: List[List[int]], info: Dict[int, int]) -> '_Tree':
"""
Builds the tree with updated transactions
:param transactions: updated transactions
:param info: support details of each item in transactions.
:return: Transactions compressed in fp-tree
"""
rootNode = _Tree()
rootNode.info = info.copy()
for i in range(len(transactions)):
rootNode.addTransaction(transactions[i], 1)
return rootNode
def __savePeriodic(self, itemSet: List[int]) -> str:
"""
The duplication items and their ranks
:param itemSet: frequent itemSet that generated
:return: patterns with original item names.
"""
temp = str()
for i in itemSet:
temp = temp + self.__rankDup[i] + "\t"
return temp
[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 program to start the operation
:return: None
"""
self.mine()
[docs]
def mine(self) -> None:
"""
main program to start the operation
:return: None
"""
global _minSup, _minWeight, _miniWeight, _maxWeight, _weights
self.__startTime = _fp._time.time()
if self._iFile is None:
raise Exception("Please enter the file path or file name:")
if self._minSup is None:
raise Exception("Please enter the Minimum Support")
self.__creatingItemSets()
self._scanningWeights()
_weights = {k: v for k, v in _weights.items() if v >= _minWeight}
_maxWeight = max([s for s in _weights.values()])
_miniWeight = min([s for s in _weights.values()])
self._minSup = self.__convert(self._minSup)
_minSup = self._minSup
itemSet = self.__frequentOneItem()
updatedTransactions = self.__updateTransactions(itemSet)
for x, y in self.__rank.items():
self.__rankDup[y] = x
info = {self.__rank[k]: v for k, v in self.__mapSupport.items()}
__Tree = self.__buildTree(updatedTransactions, info)
patterns = __Tree.generatePatterns([])
self.__finalPatterns = {}
for k in patterns:
s = self.__savePeriodic(k[0])
self.__finalPatterns[str(s)] = k[1]
print("Weighted Frequent patterns were generated successfully using basic algorithm")
self.__endTime = _fp._time.time()
self.__memoryUSS = float()
self.__memoryRSS = float()
process = _fp._psutil.Process(_fp._os.getpid())
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.replace('\t', ' '), b])
dataframe = _fp._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: csv file
:return: None
"""
self._oFile = outFile
writer = open(self._oFile, 'w+')
for x, y in self.__finalPatterns.items():
s1 = x.strip() + ":" + str(y)
writer.write("%s \n" % s1)
[docs]
def getPatterns(self) -> Dict[str, int]:
"""
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 Weighted 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(_fp._sys.argv) == 6 or len(_fp._sys.argv) == 7:
if len(_fp._sys.argv) == 7:
_ap = WFIM(_fp._sys.argv[1], _fp._sys.argv[3], _fp._sys.argv[4], _fp._sys.argv[5], _fp._sys.argv[6])
if len(_fp._sys.argv) == 6:
_ap = WFIM(_fp._sys.argv[1], _fp._sys.argv[3], _fp._sys.argv[4], _fp._sys.argv[5])
_ap.mine()
_ap.mine()
print("Total number of Weighted Frequent Patterns:", len(_ap.getPatterns()))
_ap.save(_fp._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")