# GThreePGrowth is fundamental approach to mine the partial periodic patterns in temporal database.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
# from PAMI.periodicFrequentPattern.basic import PPPGrowth as alg
#
# obj = alg.PPPGrowth(iFile, minPS, period)
#
# obj.mine()
#
# partialPeriodicPatterns = obj.getPatterns()
#
# print("Total number of partial periodic Patterns:", len(partialPeriodicPatterns))
#
# obj.save(oFile)
#
# Df = obj.getPatternInDf()
#
# 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
"""
from pandas.core.arrays import period
import deprecated
from PAMI.partialPeriodicPattern.basic import Gabstract as _abstract
from typing import List, Dict, Tuple, Set, Union, Any, Generator
import validators as _validators
from urllib.request import urlopen as _urlopen
import sys as _sys
_minPS = float()
_period = float()
_relativePS = float()
_frequentList = {}
_lno = int()
class _Node(object):
"""
A class used to represent the node of frequentPatternTree
:Attributes:
item : int
storing item of a node
timeStamps : list
To maintain the timestamps of transaction at the end of the branch
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: int, children: list)-> None:
self.item = item
self.children = children
self.parent = None
self.timeStamps = []
def addChild(self, node) -> None:
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 items
:Methods:
addTransaction(transaction)
creating transaction as a branch in frequentPatternTree
getConditionalPatterns(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: list, tid: list) -> None:
"""
adding transaction into tree
:param transaction : it represents the one transactions in database
:type transaction : list
:param tid : represents the timestamp of transaction
:type tid : list
"""
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[list, list, dict]:
"""
generates all the conditional patterns of respective node
:param alpha : it represents the Node in tree
:type alpha : Node
"""
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._conditionalTransactions(finalPatterns, finalSets, pattern)
return finalPatterns, finalSets, info
def _generateTimeStamps(self, node) -> list:
finalTs = node.timeStamps
return finalTs
def _removeNode(self, nodeValue) -> None:
"""
removing the node from tree
:param nodeValue : it represents the node in tree
:type nodeValue : node
"""
for i in self.summaries[nodeValue]:
i.parent.timeStamps = i.parent.timeStamps + i.timeStamps
del i.parent.children[nodeValue]
def _getTimeStamps(self, alpha) -> list:
"""
Returns the timeStamps of a node
:param alpha: node of tree
:return: timeStamps of a node
"""
temporary = []
for i in self.summaries[alpha]:
temporary += i.timeStamps
return temporary
def _getPeriodicSupport(self, timeStamps, pattern) -> List[float]:
"""
calculates the support and periodicity with list of timestamps
:param timeStamps : timestamps of a pattern
:type timeStamps : list
"""
global _frequentList, _lno
timeStamps.sort()
per = 0
sup = 0
for i in range(len(timeStamps) - 1):
j = i + 1
if abs(timeStamps[j] - timeStamps[i]) <= _period:
per += 1
sup += 1
l = []
for i in pattern:
l.append(_frequentList[i])
rs = per/abs(min(l) - 1)
return [per, rs]
def _conditionalTransactions(self, conditionalPatterns, conditionalTimeStamps, temp) -> Tuple[list, list, dict]:
"""
It generates the conditional patterns with periodic frequent items
:param conditionalPatterns : conditional_patterns generated from condition_pattern method for
respective node
:type conditionalPatterns : list
:param conditionalTimeStamps : represents the timestamps of conditional patterns of a node
:type conditionalTimeStamps : list
"""
global _minPS, _period
patterns = []
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._getPeriodicSupport(data1[m], temp + [m])
updatedDictionary = {k: v for k, v in updatedDictionary.items() if v[0] >= _minPS}
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), -x), reverse=True)
if len(trans) > 0:
patterns.append(trans)
timeStamps.append(conditionalTimeStamps[count])
count += 1
return patterns, timeStamps, updatedDictionary
def _generatePatterns(self, prefix) -> Generator[Tuple[list, dict], None, None]:
"""generates the patterns
:param prefix : forms the combination of items
:type prefix : list
"""
global _minPS, _relativePS
for i in sorted(self.summaries, key=lambda x: (self.info.get(x), -x)):
pattern = prefix[:]
pattern.append(i)
if self.info[i][0] >= _minPS and self.info[i][1] >= _relativePS:
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 GThreePGrowth(_abstract._partialPeriodicPatterns):
"""
:Description: 3pgrowth is fundamental approach to mine the partial periodic patterns in temporal database.
:Reference: Reference : Discovering Partial Periodic Itemsets in Temporal Databases,SSDBM '17: Proceedings of the 29th International Conference on Scientific and Statistical Database ManagementJune 2017
Article No.: 30 Pages 1–6https://doi.org/10.1145/3085504.3085535
:param iFile: str :
Name of the Input file to mine complete set of frequent pattern's
:param oFile: str :
Name of the output file to store complete set of frequent patterns
:param minPS: float:
Minimum partial periodic pattern...
:param period: float:
Minimum partial periodic...
: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:
self.iFile : file
Name of the Input file or path of the input file
self. oFile : file
Name of the output file or path of the output file
minPS: float or int or str
The user can specify minPS either in count or proportion of database size.
If the program detects the data type of minPS is integer, then it treats minPS is expressed in count.
Otherwise, it will be treated as float.
Example: minPS=10 will be treated as integer, while minPS=10.0 will be treated as float
period: float or int or str
The user can specify period either in count or proportion of database size.
If the program detects the data type of period is integer, then it treats period is expressed in count.
Otherwise, it will be treated as float.
Example: period=10 will be treated as integer, while period=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.
self.memoryUSS : float
To store the total amount of USS memory consumed by the program
self.memoryRSS : float
To store the total amount of RSS memory consumed by the program
self.startTime:float
To record the start time of the mining process
self.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
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
partialPeriodicOneItem()
Extracts the one-frequent patterns from transactions
updateTransactions()
updates the transactions by removing the aperiodic items and sort the transactions with items
by decreasing support
buildTree()
constrcuts the main tree by setting the root node as null
mine()
main program to mine the partial periodic patterns
**Executing the code on terminal:**
---------------------------------------
Format:
>>> python3 PPPGrowth.py <inputFile> <outputFile> <minPS> <period>
Examples:
>>> python3 PPPGrowth.py sampleDB.txt patterns.txt 10.0 2.0
**Sample run of the importing code:**
--------------------------------------------
.. code-block:: python
from PAMI.periodicFrequentPattern.basic import PPPGrowth as alg
obj = alg.PPPGrowth(iFile, minPS, period)
obj.mine()
partialPeriodicPatterns = obj.getPatterns()
print("Total number of partial periodic Patterns:", len(partialPeriodicPatterns))
obj.save(oFile)
Df = obj.getPatternInDf()
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
"""
_minPS = float()
_period = float()
_relativePS = {}
_startTime = float()
_endTime = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_rank = {}
_rankdup = {}
_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, _abstract._pd.DataFrame):
data, tids = [], []
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'TS' in i:
tids = self._iFile['TS'].tolist()
if 'Transactions' in i:
data = self._iFile['Transactions'].tolist()
for i in range(len(data)):
tr = [tids[i][0]]
tr = tr + data[i]
self._Database.append(tr)
self._lno = len(self._Database)
# print(self.Database)
if isinstance(self._iFile, str):
if _validators.url(self._iFile):
data = _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)
self._lno = len(self._Database)
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)
self._lno = len(self._Database)
except IOError:
print("File Not Found")
quit()
def _partialPeriodicOneItem(self) -> Tuple[dict, List[str]]:
"""
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
"""
global _frequentList
data = {}
self._period = self._convert(self._period)
self._minPS = self._convert(self._minPS)
self._relativePS = float(self._relativePS)
for tr in self._Database:
for i in range(1, len(tr)):
if tr[i] not in data:
data[tr[i]] = [0, int(tr[0]), 1]
else:
lp = int(tr[0]) - data[tr[i]][1]
if lp <= self._period:
data[tr[i]][0] += 1
data[tr[i]][1] = int(tr[0])
data[tr[i]][2] += 1
data = {k: [v[0], 1, v[2]] for k, v in data.items() if v[0] >= self._minPS}
print(len(data))
pfList = [k for k, v in sorted(data.items(), key=lambda x: x[1], reverse=True)]
self._rank = dict([(index, item) for (item, index) in enumerate(pfList)])
for x, y in self._rank.items():
_frequentList[y] = data[x][2]
return data, pfList
def _updateTransactions(self, dict1) -> List[list]:
"""
remove the items which are not frequent from transactions and updates the transactions with rank of items
:param dict1 : frequent items with support
:type dict1 : dictionary
"""
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
def _buildTree(self, data, info) -> str:
"""
it takes the transactions and support of each item and construct the main tree with setting root
node as null
:param data : it represents the one transactions in database
:type data : list
:param info : it represents the support of each item
:type info : dictionary
"""
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 pattern with its original item name
:param itemset: partial periodic pattern.
:return: pattern with original item name
"""
temp = str()
for i in itemset:
temp = temp + self._rankdup[i] + " "
return temp
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)
if '%' in value:
value = value[:-1]
value = float(int(value)/100)
else:
value = int(value)
return value
[docs]
def startMine(self) -> None:
self.mine()
[docs]
def mine(self) -> None:
"""
Main method where the patterns are mined by constructing tree.
"""
global _minPS, _period, _relativePS, _lno
self._startTime = float()
self._startTime = _abstract._time.time()
if self._iFile is None:
raise Exception("Please enter the file path or file name:")
if self._minPS is None:
raise Exception("Please enter the Minimum Support")
self._creatingItemSets()
generatedItems, pfList = self._partialPeriodicOneItem()
_minPS, _period, _relativePS, _lno = self._minPS, self._period, self._relativePS, len(self._Database)
# print(_minPS, _period, _relativePS)
updatedTransactions = self._updateTransactions(generatedItems)
for x, y in self._rank.items():
self._rankdup[y] = x
info = {self._rank[k]: v for k, v in generatedItems.items()}
Tree = self._buildTree(updatedTransactions, info)
patterns = Tree._generatePatterns([])
self._finalPatterns = {}
for i in patterns:
s = self._savePeriodic(i[0])
self._finalPatterns[s] = i[1]
self._endTime = float()
self._endTime = _abstract._time.time()
process = _abstract._psutil.Process(_abstract._os.getpid())
self._memoryUSS = float()
self._memoryRSS = float()
self._memoryUSS = process.memory_full_info().uss
self._memoryRSS = process.memory_info().rss
print("Partial Periodic Patterns were generated successfully using Generalized 3PGrowth 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):
"""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 = _abstract._pd.DataFrame(data, columns=['Patterns', 'periodicSupport'])
return dataFrame
[docs]
def save(self, outFile: str):
"""Complete set of 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 + ":" + 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) -> 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(_sys.argv) == 6 or len(_sys.argv) == 7:
if len(_sys.argv) == 7:
_ap = GThreePGrowth(_sys.argv[1], _sys.argv[3], _sys.argv[4], _sys.argv[5], _sys.argv[6])
if len(_sys.argv) == 6:
_ap = GThreePGrowth(_sys.argv[1], _sys.argv[3], _sys.argv[4], _sys.argv[5])
_ap.mine()
_Patterns = _ap.getPatterns()
print("Total number of Partial Periodic Patterns:", len(_Patterns))
_ap.save(_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:
minPS = 0.001
l = [0.2, 0.4, 0.6, 0.7, 0.8]
for i in l:
ap = GThreePGrowth('https://www.u-aizu.ac.jp/~udayrage/datasets/temporalDatabases/temporal_T10I4D100K.csv', minPS, 10000, i)
ap.mine()
Patterns = ap.getPatterns()
print("Total number of Patterns:", len(Patterns))
ap.save('/Users/Likhitha/Downloads/output')
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")