# WUFIM is one of the algorithm to discover weighted frequent patterns in an uncertain transactional database using PUF-Tree.
#
# **Importing this algorithm into a python program**
#
# from PAMI.weightedUncertainFrequentPattern.basic import basic as alg
#
# iFile = 'sampleDB.txt'
#
# minSup = 10
#
# obj = alg.basic(iFile, wFile, minSup, sep)
#
# obj.mine()
#
# Patterns = obj.getPatterns()
#
# print("Total number of Patterns:", len(Patterns))
#
# 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.weightedUncertainFrequentPattern.basic import abstract as _ab
import pandas as pd
from deprecated import deprecated
_expSup = str()
_expWSup = str()
_weights = {}
_finalPatterns = {}
_ab._sys.setrecursionlimit(20000)
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: int, probability: float) -> None:
self.item = item
self.probability = probability
class _Node(object):
"""
A class used to represent the node of frequentPatternTree
:Attributes:
item : int
storing item of a node
probability : int
To maintain the expected support of node
parent : node
To maintain the parent of every node
children : list
To maintain the children of node
:Methods:
addChild(itemName)
storing the children to their respective parent nodes
"""
def __init__(self, item, children: list) -> None:
self.item = item
self.probability = 1
self.children = children
self.parent = None
def addChild(self, node) -> None:
"""
This method is used to add a child node to the current node in the frequent pattern tree.
:param node:The node to be added as a child
:type node:_Node
:return: None
"""
self.children[node.item] = node
node.parent = self
#global _finalPatterns, _expSup, _expWSup, _weights
class _Tree(object):
"""
A class used to represent the frequentPatternGrowth tree structure
:Attributes:
root : Node
Represents the root node of the tree
summaries : dictionary
storing the nodes with same item name
info : dictionary
stores the support of items
:Methods:
addTransaction(transaction)
creating transaction as a branch in frequentPatternTree
addConditionalPattern(prefixPaths, supportOfItems)
construct the conditional tree for prefix paths
conditionalPatterns(Node)
generates the conditional patterns from tree for specific node
conditionalTransactions(prefixPaths,Support)
takes the prefixPath of a node and support at child of the path and extract the frequent items from prefixPaths and generates prefixPaths with items which are frequent
remove(Node)
removes the node from tree once after generating all the patterns respective to the node
generatePatterns(Node)
starts from the root node of the tree and mines the frequent patterns
"""
def __init__(self) -> None:
self.root = _Node(None, {})
self.summaries = {}
self.info = {}
def addTransaction(self, transaction) -> None:
"""
Adding transaction into tree
:param transaction: it represents the one self.Database in database
:type transaction: list
:return: None
"""
currentNode = self.root
for i in range(len(transaction)):
if transaction[i].item not in currentNode.children:
newNode = _Node(transaction[i].item, {})
l1 = i - 1
lp = []
while l1 >= 0:
lp.append(transaction[l1].probability)
l1 -= 1
if len(lp) == 0:
newNode.probability = transaction[i].probability
else:
newNode.probability = max(lp) * transaction[i].probability
currentNode.addChild(newNode)
if transaction[i].item in self.summaries:
self.summaries[transaction[i].item].append(newNode)
else:
self.summaries[transaction[i].item] = [newNode]
currentNode = newNode
else:
currentNode = currentNode.children[transaction[i].item]
l1 = i - 1
lp = []
while l1 >= 0:
lp.append(transaction[l1].probability)
l1 -= 1
if len(lp) == 0:
currentNode.probability += transaction[i].probability
else:
currentNode.probability += max(lp) * transaction[i].probability
def addConditionalPattern(self, transaction, sup) -> None:
"""
constructing conditional tree from prefixPaths
:param transaction : it represents the one self.Database in database
:type transaction : list
:param sup : support of prefixPath taken at last child of the path
:type sup : int
:return: None
"""
# This method takes transaction, support and constructs the conditional tree
currentNode = self.root
for i in range(len(transaction)):
if transaction[i] not in currentNode.children:
newNode = _Node(transaction[i], {})
newNode.probability = sup
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.probability += sup
def conditionalPatterns(self, alpha) -> tuple:
"""
generates all the conditional patterns of respective node
:param alpha : it represents the Node in tree
:type alpha : _Node
:return: tuple
"""
# This method generates conditional patterns of node by traversing the tree
finalPatterns = []
sup = []
for i in self.summaries[alpha]:
s = i.probability
set2 = []
while i.parent.item is not None:
set2.append(i.parent.item)
i = i.parent
if len(set2) > 0:
set2.reverse()
finalPatterns.append(set2)
sup.append(s)
finalPatterns, support, info = self.conditionalTransactions(finalPatterns, sup)
return finalPatterns, support, info
def removeNode(self, nodeValue) -> None:
"""
Removing the node from tree
:param nodeValue : it represents the node in tree
:type nodeValue : node
:return: None
"""
for i in self.summaries[nodeValue]:
del i.parent.children[nodeValue]
def conditionalTransactions(self, condPatterns, support) -> tuple:
"""
It generates the conditional patterns with frequent items
:param condPatterns : conditionalPatterns generated from conditionalPattern method for respective node
:type condPatterns : list
:param support : the support of conditional pattern in tree
:type support : int
:return: tuple
"""
global _expSup, _expWSup
pat = []
sup = []
count = {}
for i in range(len(condPatterns)):
for j in condPatterns[i]:
if j in count:
count[j] += support[i]
else:
count[j] = support[i]
updatedDict = {}
updatedDict = {k: v for k, v in count.items() if v >= _expSup}
count = 0
for p in condPatterns:
p1 = [v for v in p if v in updatedDict]
trans = sorted(p1, key=lambda x: updatedDict[x], reverse=True)
if len(trans) > 0:
pat.append(trans)
sup.append(support[count])
count += 1
return pat, sup, updatedDict
def generatePatterns(self, prefix) -> None:
"""
Generates the patterns
:param prefix : forms the combination of items
:type prefix : list
:return: None
"""
global _finalPatterns, _expSup, _expWSup, _weights
for i in sorted(self.summaries, key=lambda x: (self.info.get(x))):
pattern = prefix[:]
pattern.append(i)
weight = 0
for k in pattern:
weight = weight + _weights[k]
weight = weight/len(pattern)
if self.info.get(i) >= _expSup and self.info.get(i) * weight >= _expWSup:
_finalPatterns[tuple(pattern)] = self.info.get(i)
patterns, support, info = self.conditionalPatterns(i)
conditionalTree = _Tree()
conditionalTree.info = info.copy()
for pat in range(len(patterns)):
conditionalTree.addConditionalPattern(patterns[pat], support[pat])
if len(patterns) > 0:
conditionalTree.generatePatterns(pattern)
self.removeNode(i)
[docs]
class WUFIM(_ab._weightedFrequentPatterns):
"""
About this algorithm
====================
:Description: It is one of the algorithm to discover weighted frequent patterns in a uncertain transactional database using PUF-Tree.
:Reference: Efficient Mining of Weighted Frequent Itemsets in Uncertain Databases.
In : Machine Learning and Data Mining in Pattern Recognition book Chun-Wei Jerry Lin, Wensheng Gan, Philippe Fournier Viger, Tzung-Pei Hong
:param iFile: str :
Name of the Input file to mine complete set of Weighted Uncertain Periodic Frequent Patterns
:param oFile: str :
Name of the output file to store complete set of Weighted Uncertain Periodic Frequent Patterns
:param minSup: str:
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.
:param wFile: str :
This is a weighted file.
:Attributes:
iFile : file
Name of the Input file or path of the input file
wFile : 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
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(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 basic.py <inputFile> <outputFile> <minSup>
Example Usage:
(.venv) $ python3 basic.py sampleTDB.txt patterns.txt 3
.. note:: minSup will be considered in support count or frequency
**Calling from a python program**
.. code-block:: python
from PAMI.weightedUncertainFrequentPattern.basic import basic as alg
iFile = 'sampleDB.txt'
minSup = 10 # can also be specified between 0 and 1
obj = alg.basic(iFile, wFile, expSup, expWSup)
obj.mine()
Patterns = obj.getPatterns()
print("Total number of Patterns:", len(Patterns))
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)
"""
_startTime = float()
_endTime = float()
_minSup = str()
_finalPatterns = {}
_iFile = " "
_wFile = " "
_oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_rank = {}
_expSup = float()
_expWSup = float()
#Database1 = None
def __init__(self, iFile, wFile, expSup, expWSup, sep='\t') -> None:
super().__init__(iFile, wFile, expSup, expWSup, sep)
def _creatingItemSets(self) -> None:
"""
Scans the uncertain transactional dataset
:return: None
"""
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 = line.decode("utf-8")
line = line.strip()
line = [i for i in line.split(':')]
temp1 = [i.rstrip() for i in line[0].split(self._sep)]
temp2 = [i.rstrip() for i in line[1].split(self._sep)]
temp1 = [x for x in temp1 if x]
temp2 = [x for x in temp2 if x]
tr = []
for i in range(len(temp1)):
item = temp1[i]
probability = float(temp2[i])
product = _Item(item, probability)
tr.append(product)
self._Database.append(tr)
else:
try:
with open(self._iFile, 'r') as f:
for line in f:
line = line.strip()
line = [i for i in line.split(':')]
temp1 = [i.rstrip() for i in line[0].split(self._sep)]
temp2 = [i.rstrip() for i in line[1].split(self._sep)]
temp1 = [x for x in temp1 if x]
temp2 = [x for x in temp2 if x]
tr = []
for i in range(len(temp1)):
item = temp1[i]
probability = float(temp2[i])
product = _Item(item, probability)
tr.append(product)
self._Database.append(tr)
except IOError:
print("File Not Found")
def _scanningWeights(self) -> None:
"""
Scans the uncertain transactional dataset
:return: None
"""
self._weights = {}
if isinstance(self._wFile, _ab._pd.DataFrame):
weights, data = [], []
if self._wFile.empty:
print("its empty..")
i = self._wFile.columns.values.tolist()
if 'items' in i:
data = self._wFile['items'].tolist()
if 'weights' in i:
weights = self._wFile['weights'].tolist()
for k in range(len(data)):
self._weights[data[k]] = int(float(weights[k]))
# print(self.Database)
if isinstance(self._wFile, str):
if _ab._validators.url(self._wFile):
data = _ab._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]
self._weights[temp[0]] = int(float(temp[1]))
else:
try:
with open(self._wFile, '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]
self._weights[temp[0]] = float(temp[1])
except IOError:
print("File Not Found")
def _frequentOneItem(self) -> tuple:
"""
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
:return: tuple
"""
mapSupport = {}
for i in self._Database:
for j in i:
if j.item not in mapSupport:
if self._weights.get(j.item) is not None:
mapSupport[j.item] = [j.probability, self._weights[j.item]]
else:
mapSupport[j.item][0] += j.probability
mapSupport = {k: v[0] for k, v in mapSupport.items() if v[0] >= self._expSup and v[0] * v[1] >= self._expWSup}
plist = [k for k, v in sorted(mapSupport.items(), key=lambda x: x[1], reverse=True)]
self.rank = dict([(index, item) for (item, index) in enumerate(plist)])
return mapSupport, plist
@staticmethod
def _buildTree(data, info) -> _Tree:
"""
It takes the self.Database and support of each item and construct the main tree with setting root node as null
:param data : it represents the one self.Database in database
:type data : list
:param info : it represents the support of each item
:type info : dictionary
:return: tree
"""
rootNode = _Tree()
rootNode.info = info.copy()
for i in range(len(data)):
rootNode.addTransaction(data[i])
return rootNode
def _updateTransactions(self, dict1) -> list:
"""
Remove the items which are not frequent from self.Database and updates the self.Database with rank of items
:param dict1 : frequent items with support
:type dict1 : dictionary
:return: list
"""
list1 = []
for tr in self._Database:
list2 = []
for i in range(0, len(tr)):
if tr[i].item in dict1:
list2.append(tr[i])
if len(list2) >= 2:
basket = list2
basket.sort(key=lambda val: self.rank[val.item])
list2 = basket
list1.append(list2)
return list1
@staticmethod
def _check(i, x) -> int:
"""
To check the presence of item or pattern in transaction
:param x: it represents the pattern
:type x : list
:param i : represents the uncertain self.Database
:type i : list
:return: integer number
"""
# This method taken a transaction as input and returns the tree
for m in x:
k = 0
for n in i:
if m == n.item:
k += 1
if k == 0:
return 0
return 1
def _convert(self, value) -> 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 = (len(self._Database) * value)
if type(value) is str:
if '.' in value:
value = (len(self._Database) * value)
else:
value = int(value)
return value
#global _finalPatterns
def _removeFalsePositives(self) -> None:
"""
To remove the false positive patterns generated in frequent patterns.
:return: patterns with accurate probability
"""
global _finalPatterns
periods = {}
for i in self._Database:
for x, y in _finalPatterns.items():
if len(x) == 1:
periods[x] = y
else:
s = 1
check = self._check(i, x)
if check == 1:
for j in i:
if j.item in x:
s *= j.probability
if x in periods:
periods[x] += s
else:
periods[x] = s
for x, y in periods.items():
weight = 0
for i in x:
weight += self._weights[i]
weight = weight / len(x)
if weight * y >= self._expWSup:
sample = str()
for i in x:
sample = sample + i + "\t"
self._finalPatterns[sample] = y
[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:
"""
mine() 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:
"""
mine() method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patternS
"""
global _expSup, _expWSup, _weights, _finalPatterns
self._startTime = _ab._time.time()
self._Database, self._weights = [], {}
self._creatingItemSets()
self._scanningWeights()
_weights = self._weights
self._expSup = float(self._expSup)
self._expWSup = float(self._expWSup)
_expSup = self._expSup
_expWSup = self._expWSup
self._finalPatterns = {}
mapSupport, plist = self._frequentOneItem()
self.Database1 = self._updateTransactions(mapSupport)
info = {k: v for k, v in mapSupport.items()}
Tree1 = self._buildTree(self.Database1, info)
Tree1.generatePatterns([])
self._removeFalsePositives()
print("Weighted Frequent patterns were generated successfully using basic 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():
s = str()
for i in a:
s = s + i + " "
data.append([s, 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: Specify 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():
s = str()
for i in x:
s = s + i + "\t"
s1 = s.strip() + ":" + str(y)
writer.write("%s \n" % s1)
[docs]
def getPatterns(self) -> dict:
"""
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 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) == 6 or len(_ab._sys.argv) == 7:
if len(_ab._sys.argv) == 7:
_ap = WUFIM(_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 = WUFIM(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5])
_ap.mine()
_ap.mine()
print("Total number of Weighted Uncertain 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:
for k in [120, 140, 160, 180, 200]:
_ap = WUFIM('/Users/likhitha/Downloads/uncertainTransaction_T10I4D200K.csv', '/Users/likhitha/Downloads/T10_weights.txt',
k, 500, '\t')
_ap.mine()
print("Total number of Weighted Uncertain Frequent Patterns:", len(_ap.getPatterns()))
_ap.save('/Users/likhitha/Downloads/WUFIM_output.txt')
print("Total Memory in USS:", _ap.getMemoryUSS())
print("Total Memory in RSS", _ap.getMemoryRSS())
print("Total ExecutionTime in ms:", _ap.getRuntime())
print("Error! The number of input parameters do not match the total number of parameters provided")