# EPCPGrowth is an algorithm to discover periodic-Correlated patterns in a temporal database.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
#
# from PAMI.periodicCorrelatedPattern.basic import EPCPGrowth as alg
#
# obj = alg.EPCPGrowth(iFile, minSup, minAllCOnf, maxPer, maxPerAllConf)
#
# obj.mine()
#
# periodicCorrelatedPatterns = obj.getPatterns()
#
# print("Total number of Periodic Frequent Patterns:", len(periodicCorrelatedPatterns))
#
# 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/>.
Copyright (C) 2021 Rage Uday Kiran
"""
import sys
from PAMI.periodicCorrelatedPattern.basic import abstract as _ab
from typing import List, Dict, Tuple, Set, Union, Any, Generator
import pandas as pd
_maxPer = float()
_minAllConf = float()
_minSup = float()
_maxPerAllConf = float()
_frequentList = {}
_lno = int()
class _Node(object):
"""
A class used to represent the node of frequentPatternTree
:Attributes:
item : int or None
Storing item of a node
timeStamps : list
To maintain the timestamps of a database at the end of the branch
parent : node
To maintain the parent of every node
children : list
To maintain the children of a node
:Methods:
addChild(itemName)
Storing the children to their respective parent nodes
"""
def __init__(self, item, children) -> None:
"""
Initializing the Node class
:param item: Storing the item of a node
:type item: int or None
:param children: To maintain the children of a node
:type children: dict
"""
self.item = item
self.children = children
self.parent = None
self.timeStamps = []
def addChild(self, node) -> None:
"""
To add the children to a node
:param node: parent node in the tree
"""
self.children[node.item] = node
node.parent = self
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 the items
:Methods:
addTransactions(Database)
Creating transaction as a branch in frequentPatternTree
getConditionalPatterns(Node)
Generates the conditional patterns from tree for specific node
conditionalTransaction(prefixPaths,Support)
Takes the prefixPath of a node and support at child of the path and extract the frequent patterns 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 periodic-frequent patterns
"""
def __init__(self) -> None:
self.root = _Node(None, {})
self.summaries = {}
self.info = {}
def addTransaction(self, transaction, tid) -> None:
"""
Adding a transaction into tree
:param transaction: To represent the complete database
:type transaction: list
:param tid: To represent the timestamp of a database
:type tid: list
:return: pfp-growth tree
"""
currentNode = self.root
for i in range(len(transaction)):
if transaction[i] not in currentNode.children:
newNode = _Node(transaction[i], {})
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.timeStamps = currentNode.timeStamps + tid
def getConditionalPatterns(self, alpha, pattern) -> tuple:
"""
Generates all the conditional patterns of a respective node
:param alpha: To represent a Node in the tree
:type alpha: Node
:param pattern: pattern
:type pattern: list
:return: A tuple consisting of finalPatterns, conditional pattern base and information
"""
finalPatterns = []
finalSets = []
for i in self.summaries[alpha]:
set1 = i.timeStamps
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)
finalSets.append(set1)
finalPatterns, finalSets, info = self.conditionalDatabases(finalPatterns, finalSets, pattern)
return finalPatterns, finalSets, info
@staticmethod
def generateTimeStamps(node) -> list:
"""
To get the timestamps of a node
:param node: A node in the tree
:return: Timestamps of a node
"""
finalTimeStamps = node.timeStamps
return finalTimeStamps
def removeNode(self, nodeValue) -> None:
"""
Removing the node from tree
:param nodeValue: To represent a node in the tree
:type nodeValue: node
:return: Tree with their nodes updated with timestamps
"""
for i in self.summaries[nodeValue]:
i.parent.timeStamps = i.parent.timeStamps + i.timeStamps
del i.parent.children[nodeValue]
def getTimeStamps(self, alpha) -> list:
"""
To get all the timestamps of the nodes which share same item name
:param alpha: Node in a tree
:return: Timestamps of a node
"""
temporary = []
for i in self.summaries[alpha]:
temporary += i.timeStamps
return temporary
@staticmethod
def getSupportAndPeriod(timeStamps, pattern) -> list:
"""
To calculate the periodicity and support
:param timeStamps: Timestamps of an item set
:param pattern: pattern
:return: support, periodicity
"""
global _minSup, _minAllConf, _maxPer, _maxPerAllConf, _frequentList, _lno
timeStamps.sort()
cur = 0
per = list()
sup = 0
for j in range(len(timeStamps)):
per.append(timeStamps[j] - cur)
cur = timeStamps[j]
sup += 1
per.append(_lno - cur)
if len(per) == 0:
return [0, 0, 0, 0]
l = []
for i in pattern:
l.append(_frequentList[i][0])
l1 = []
for i in pattern:
l1.append(_frequentList[i][1])
conf = sup/max(l)
perConf = max(per)/min(l1)
#print(pattern, timeStamps, l, l1, sup, max(per), conf, perConf)
return [sup, max(per), conf, perConf]
def conditionalDatabases(self, conditionalPatterns: list, conditionalTimeStamps: list, pattern) -> tuple:
"""
It generates the conditional patterns with periodic-frequent items
:param conditionalPatterns: conditionalPatterns generated from conditionPattern method of a respective node
:type conditionalPatterns: list
:param conditionalTimeStamps: Represents the timestamps of a conditional patterns of a node
:type conditionalTimeStamps: list
:param pattern: pattern
:type pattern: list
:returns: Returns conditional transactions by removing non-periodic and non-frequent items
"""
global _maxPer, _minSup
#temp = pattern
pat = []
timeStamps = []
data1 = {}
for i in range(len(conditionalPatterns)):
for j in conditionalPatterns[i]:
if j in data1:
data1[j] = data1[j] + conditionalTimeStamps[i]
else:
data1[j] = conditionalTimeStamps[i]
updatedDictionary = {}
for m in data1:
updatedDictionary[m] = self.getSupportAndPeriod(data1[m], pattern + [m])
updatedDictionary = {k: v for k, v in updatedDictionary.items() if v[0] >= _minSup and v[1] <= _maxPer}
count = 0
for p in conditionalPatterns:
p1 = [v for v in p if v in updatedDictionary]
trans = sorted(p1, key=lambda x: (updatedDictionary.get(x)[0], -x), reverse=True)
if len(trans) > 0:
pat.append(trans)
timeStamps.append(conditionalTimeStamps[count])
count += 1
return pat, timeStamps, updatedDictionary
def generatePatterns(self, prefix: list) -> Generator:
"""
Generates the patterns
:param prefix: Forms the combination of items
:type prefix: list
:returns: yields patterns with their support and periodicity
"""
global _minSup, _minAllConf, _maxPer, _maxPerAllConf
for i in sorted(self.summaries, key=lambda x: (self.info.get(x)[0], -x)):
pattern = prefix[:]
pattern.append(i)
#print(pattern, self.info[i][0], self.info[i][1], self.info[i][2], self.info[i][3])
if self.info[i][0] >= _minSup and self.info[i][1] <= _maxPer and self.info[i][2] >= _minAllConf and self.info[i][3] <= _maxPerAllConf:
yield pattern, self.info[i]
patterns, timeStamps, info = self.getConditionalPatterns(i, pattern)
conditionalTree = _Tree()
conditionalTree.info = info.copy()
for pat in range(len(patterns)):
conditionalTree.addTransaction(patterns[pat], timeStamps[pat])
if len(patterns) > 0:
for q in conditionalTree.generatePatterns(pattern):
yield q
self.removeNode(i)
[docs]
class EPCPGrowth(_ab._periodicCorrelatedPatterns):
"""
:Description: EPCPGrowth is an algorithm to discover periodic-Correlated patterns in a temporal database.
:Reference: http://www.tkl.iis.u-tokyo.ac.jp/new/uploads/publication_file/file/897/Venkatesh2018_Chapter_DiscoveringPeriodic-Correlated.pdf
: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
minAllConf : int or float or str
The user can specify minAllConf either in count or proportion of database size.
If the program detects the data type of minAllConf is integer, then it treats minAllCOnf is expressed in count.
Otherwise, it will be treated as float.
Example: minAllCOnf=10 will be treated as integer, while minAllConf=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
maxPerAllConf : int or float or str
The user can specify maxPerAllConf either in count or proportion of database size.
If the program detects the data type of maaxPerAllConf is integer, then it treats maxPerAllConf is expressed in count.
Otherwise, it will be treated as float.
Example : maxPerAllConf=10 will be treated as integer, while maxPerAllConf=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 periodic-frequent patterns will be loaded in to a 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(fileName)
Scans the dataset and stores in a list format
PeriodicFrequentOneItem()
Extracts the one-periodic-frequent patterns from database
updateDatabases()
Update the database by removing aperiodic 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
**Executing the code on terminal:**
---------------------------------------
Format:
>>> python3 PFPGrowth.py <inputFile> <outputFile> <minSup> <maxPer>
Examples:
>>> python3 PFPGrowth.py sampleTDB.txt patterns.txt 0.3 0.4
**Sample run of importing the code:**
----------------------------------------
.. code-block:: python
from PAMI.periodicCorrelatedPattern.basic import EPCPGrowth as alg
obj = alg.EPCPGrowth(iFile, minSup, minAllCOnf, maxPer, maxPerAllConf)
obj.mine()
periodicCorrelatedPatterns = obj.getPatterns()
print("Total number of Periodic Frequent Patterns:", len(periodicCorrelatedPatterns))
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.\n
"""
_startTime = float()
_endTime = float()
_minSup = str()
_minAllCOnf = float()
_maxPer = float()
_maxPerAllConf = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_rank = {}
_rankedUp = {}
_lno = 0
def _creatingItemSets(self) -> None:
"""
Storing the complete transactions of the database/input file in a database variable
"""
self._Database = []
if isinstance(self._iFile, _ab._pd.DataFrame):
data, ts = [], []
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'TS' in i:
ts = self._iFile['TS'].tolist()
if 'Transactions' in i:
data = self._iFile['Transactions'].tolist()
for i in range(len(data)):
tr = [ts[i][0]]
tr = tr + data[i]
self._Database.append(tr)
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 _periodicFrequentOneItem(self) -> tuple:
"""
Calculates the support of each item in the database and assign ranks to the items
by decreasing support and returns the frequent items list
:returns: return the one-length periodic frequent patterns
"""
global _frequentList
data = {}
for tr in self._Database:
for i in range(1, len(tr)):
if tr[i] not in data:
data[tr[i]] = [int(tr[0]), int(tr[0]), 1]
else:
data[tr[i]][0] = max(data[tr[i]][0], (int(tr[0]) - data[tr[i]][1]))
data[tr[i]][1] = int(tr[0])
data[tr[i]][2] += 1
for key in data:
data[key][0] = max(data[key][0], abs(len(self._Database) - data[key][1]))
data = {k: [v[2], v[0], 1, 1] for k, v in data.items() if v[0] <= self._maxPer and v[2] >= self._minSup}
pfList = [k for k, v in sorted(data.items(), key=lambda x_: (x_[1][0], x_[0]), reverse=True)]
self._rank = dict([(index, item) for (item, index) in enumerate(pfList)])
for x, y in self._rank.items():
_frequentList[y] = data[x]
return data, pfList
def _updateDatabases(self, dict1) -> list:
"""
Remove the items which are not frequent from database and updates the database with rank of items
:param dict1: frequent items with support
:type dict1: dictionary
:return: Sorted and updated transactions
"""
list1 = []
for tr in self._Database:
list2 = [int(tr[0])]
for i in range(1, len(tr)):
if tr[i] in dict1:
list2.append(self._rank[tr[i]])
if len(list2) >= 2:
basket = list2[1:]
basket.sort()
list2[1:] = basket[0:]
list1.append(list2)
return list1
@staticmethod
def _buildTree(data, info) -> _Tree:
"""
It takes the database and support of each item and construct the main tree by setting root node as a null
:param data: it represents the one Database in database
:type data: list
:param info: it represents the support of each item
:type info: dictionary
:return: returns root node of tree
"""
rootNode = _Tree()
rootNode.info = info.copy()
for i in range(len(data)):
set1 = [data[i][0]]
rootNode.addTransaction(data[i][1:], set1)
return rootNode
def _savePeriodic(self, itemSet) -> str:
"""
To convert the ranks of items in to their original item names
:param itemSet: frequent pattern.
:return: frequent pattern with original item names
"""
t1 = str()
for i in itemSet:
t1 = t1 + self._rankedUp[i] + "\t"
return t1
def _convert(self, value) -> float:
"""
To convert the given user specified value
:param value: user specified value
:return: converted 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 = float(value)
value = (len(self._Database) * value)
else:
value = int(value)
return value
[docs]
def startMine(self) -> None:
"""
Mining process will start from this function
"""
global _minSup, _maxPer, _minAllConf, _maxPerAllConf, _lno
self._startTime = _ab._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._minSup = self._convert(self._minSup)
self._minAllConf = float(self._minAllConf)
self._maxPer = self._convert(self._maxPer)
self._maxPerAllConf = float(self._maxPerAllConf)
_minSup, _minAllConf, _maxPer, _maxPerAllConf, _lno = self._minSup, self._minAllConf, self._maxPer, self._maxPerAllConf, len(self._Database)
#print(_minSup, _minAllConf, _maxPer, _maxPerAllConf)
if self._minSup > len(self._Database):
raise Exception("Please enter the minSup in range between 0 to 1")
generatedItems, pfList = self._periodicFrequentOneItem()
updatedDatabases = self._updateDatabases(generatedItems)
for x, y in self._rank.items():
self._rankedUp[y] = x
info = {self._rank[k]: v for k, v in generatedItems.items()}
Tree = self._buildTree(updatedDatabases, info)
patterns = Tree.generatePatterns([])
self._finalPatterns = {}
for i in patterns:
sample = self._savePeriodic(i[0])
self._finalPatterns[sample] = i[1]
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("Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm ")
[docs]
def mine(self) -> None:
"""
Mining process will start from this function
"""
global _minSup, _maxPer, _minAllConf, _maxPerAllConf, _lno
self._startTime = _ab._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._minSup = self._convert(self._minSup)
self._minAllConf = float(self._minAllConf)
self._maxPer = self._convert(self._maxPer)
self._maxPerAllConf = float(self._maxPerAllConf)
_minSup, _minAllConf, _maxPer, _maxPerAllConf, _lno = self._minSup, self._minAllConf, self._maxPer, self._maxPerAllConf, len(self._Database)
#print(_minSup, _minAllConf, _maxPer, _maxPerAllConf)
if self._minSup > len(self._Database):
raise Exception("Please enter the minSup in range between 0 to 1")
generatedItems, pfList = self._periodicFrequentOneItem()
updatedDatabases = self._updateDatabases(generatedItems)
for x, y in self._rank.items():
self._rankedUp[y] = x
info = {self._rank[k]: v for k, v in generatedItems.items()}
Tree = self._buildTree(updatedDatabases, info)
patterns = Tree.generatePatterns([])
self._finalPatterns = {}
for i in patterns:
sample = self._savePeriodic(i[0])
self._finalPatterns[sample] = i[1]
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("Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm ")
[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 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], b[2], b[3]])
dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support', 'Periodicity', 'allConf', 'maxPerAllConf'])
return dataFrame
[docs]
def save(self, outFile: str) -> None:
"""
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]) + ":" + str(y[2]) + ":" + str(y[3])
writer.write("%s \n" % s1)
[docs]
def getPatterns(self) -> dict:
""" Function to send the set of periodic-frequent patterns after completion of the mining process
:return: returning periodic-frequent patterns
:rtype: dict
"""
return self._finalPatterns
[docs]
def printResults(self) -> None:
"""
This function is used to print thr results
"""
print("Total number of Correlated Periodic-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 = EPCPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5], sys.argv[6], sys.argv[7])
if len(_ab._sys.argv) == 7:
_ap = EPCPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], sys.argv[5], sys.argv[6])
_ap.mine()
print("Total number of Correlated Periodic-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:
print("Error! The number of input parameters do not match the total number of parameters provided")