# UPFPGrowth is used to discover periodic-frequent patterns in an uncertain temporal database.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
#
# from PAMI.uncertainPeriodicFrequentPattern.basic import UPFPGrowth as alg
#
# iFile = 'sampleDB.txt'
#
# minSup = 10 # can also be specified between 0 and 1
#
# maxPer = 2 # can also be specified between 0 and 1
#
# obj = alg.UPFPGrowth(iFile, minSup, maxPer)
#
# obj.mine()
#
# periodicFrequentPatterns = obj.getPatterns()
#
# print("Total number of Periodic Frequent Patterns:", len(periodicFrequentPatterns))
#
# 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/>.
"""
import pandas as pd
from deprecated import deprecated
from PAMI.uncertainPeriodicFrequentPattern.basic import abstract as _ab
from typing import List, Dict, Tuple, Union
#_minSup = float()
__maxPer = float()
__first = int()
#_last = int()
__lno = int()
global _lno, _maxPer, _minSup, _first, _last, periodic
#rank = {}
#periodic = {}
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: str, 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
timeStamps: list
To maintain the timeStamps of node
:Methods:
addChild(itemName)
storing the children to their respective parent nodes
"""
def __init__(self, item: str, children: Dict) -> None:
self.item = item
self.probability = 1
self.children = children
self.parent = None
self.timeStamps = []
def addChild(self, node: '_Node') -> None:
"""
To add the children details to parent node
:param node: children node
:return: updated parent node children
"""
self.children[node.item] = node
node.parent = self
def _printTree(root) -> None:
"""
To print the details of tree
:param root: root node of the tree
:return: details of tree
"""
for x, y in root.children.items():
print(x, y.item, y.probability, y.parent.item, y.timeStamps)
_printTree(y)
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:
addTransactions(transaction)
creating transaction as a branch in frequentPatternTree
addConditionalTransaction(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 addTransactions(self, transaction: List['_Item'], tid: int) -> None:
"""
Adding transaction into tree
:param transaction: it represents the one transaction in database
:type transaction: list
:param tid: the timestamp of transaction
:type tid: 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
temp = []
while l1 >= 0:
temp.append(transaction[l1].probability)
l1 -= 1
if len(temp) == 0:
newNode.probability = transaction[i].probability
else:
newNode.probability = max(temp) * 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
temp = []
while l1 >= 0:
temp.append(transaction[l1].probability)
l1 -= 1
if len(temp) == 0:
currentNode.probability += transaction[i].probability
else:
currentNode.probability += max(temp) * transaction[i].probability
currentNode.timeStamps = currentNode.timeStamps + tid
def addConditionalTransaction(self, transaction: List[str], ts: List[int], sup: float) -> None:
"""
Constructing conditional tree from prefixPaths
:param transaction : it represents the one transaction in database
:type transaction : list
:param ts: timeStamp of a transaction
:type ts: list
:param sup : support of prefixPath taken at last child of the path
:type sup : int
:return: None
"""
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
currentNode.timeStamps = currentNode.timeStamps + ts
def getConditionalPatterns(self, alpha: str) -> Tuple[List[List[str]], List[List[int]], List[float], Dict[str, List[float]]]:
"""
Generates all the conditional patterns of respective node.
:param alpha : it represents the Node in tree
:type alpha : Node
:return: tuple
"""
finalPatterns = []
finalTimeStamps = []
sup = []
for i in self.summaries[alpha]:
set1 = i.timeStamps
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)
finalTimeStamps.append(set1)
sup.append(s)
finalPatterns, finalTimeStamps, support, info = self.conditionalTransactions(finalPatterns, finalTimeStamps,
sup)
return finalPatterns, finalTimeStamps, support, info
def removeNode(self, nodeValue: str) -> 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]:
i.parent.timeStamps = i.parent.timeStamps + i.timeStamps
del i.parent.children[nodeValue]
def getPeriodAndSupport(self, s: float, timeStamps: List[int]) -> List[float]:
"""
Calculates the period and support of an item based on the given support value and list of timestamps.
:param s: The support value.
:type s: float
:param timeStamps: A list of timestamps.
:type timeStamps: List[int]
:return: A list containing the support and period of the item.
:rtype: List[float]
"""
global _lno, _maxPer
timeStamps.sort()
cur = 0
per = 0
sup = s
for j in range(len(timeStamps)):
per = max(per, timeStamps[j] - cur)
if per > _maxPer:
return [0, 0]
cur = timeStamps[j]
per = max(per, _lno - cur)
return [sup, per]
def conditionalTransactions(self, condPatterns: List[List[str]], condTimeStamps: List[List[int]], support: List[float]) -> Tuple[List[List[str]], List[List[int]], List[float], Dict[str, List[float]]]:
"""
It generates the conditional patterns with frequent items
:param condPatterns : conditional patterns generated from getConditionalPatterns method for respective node
:type condPatterns : list
:param condTimeStamps: timeStamps of conditional transactions
:type condTimeStamps: list
:param support : the support of conditional pattern in tree
:type support : list
"""
global _minSup, _maxPer
pat = []
timeStamps = []
sup = []
data1 = {}
count = {}
for i in range(len(condPatterns)):
for j in condPatterns[i]:
if j in data1:
data1[j] = data1[j] + condTimeStamps[i]
count[j] += support[i]
else:
data1[j] = condTimeStamps[i]
count[j] = support[i]
updatedDict = {}
for m in data1:
updatedDict[m] = self.getPeriodAndSupport(count[m], data1[m])
updatedDict = {k: v for k, v in updatedDict.items() if v[0] >= _minSup and v[1] <= _maxPer}
count = 0
for p in condPatterns:
p1 = [v for v in p if v in updatedDict]
trans = sorted(p1, key=lambda x: (updatedDict.get(x)[0]), reverse=True)
if len(trans) > 0:
pat.append(trans)
timeStamps.append(condTimeStamps[count])
sup.append(support[count])
count += 1
return pat, timeStamps, sup, updatedDict
def generatePatterns(self, prefix: List[str], periodic: Dict) -> None:
"""
Generates the patterns
:param prefix : forms the combination of items
:type prefix : list
:param periodic: periodic patterns
:type periodic: dictionary
:return: None
"""
global _minSup
for i in sorted(self.summaries, key=lambda x: (self.info.get(x)[0])):
pattern = prefix[:]
pattern.append(i)
s = 0
for x in self.summaries[i]:
s += x.probability
periodic[tuple(pattern)] = self.info[i]
if s >= _minSup:
patterns, timeStamps, support, info = self.getConditionalPatterns(i)
conditionalTree = _Tree()
conditionalTree.info = info.copy()
for pat in range(len(patterns)):
conditionalTree.addConditionalTransaction(patterns[pat], timeStamps[pat], support[pat])
if len(patterns) > 0:
conditionalTree.generatePatterns(pattern, periodic)
self.removeNode(i)
[docs]
class UPFPGrowth(_ab._periodicFrequentPatterns):
"""
About this algorithm
====================
:Description: Basic is to discover periodic-frequent patterns in a uncertain temporal database.
:Reference: Uday Kiran, R., Likhitha, P., Dao, MS., Zettsu, K., Zhang, J. (2021).Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases.
In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing.
ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham.
https://doi.org/10.1007/978-3-030-92307-5_83
:param iFile: str :
Name of the Input file to mine complete set of Uncertain Periodic Frequent Patterns
:param oFile: str :
Name of the output file to store complete set of Uncertain Periodic Frequent patterns
:param minSup: 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.
:param maxper: float :
where maxPer represents the maximum periodicity threshold value specified by the user.
:Attributes:
iFile : file
Name of the Input file or path of the input file
oFile : file
Name of the output file or path of 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
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
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
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()
Scans the dataset and stores in a list format
PeriodicFrequentOneItem()
Extracts the one-periodic-frequent patterns from database
updateTransaction()
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
removeFalsePositives()
To remove the false positives in generated patterns
Execution methods
=================
**Terminal command**
.. code-block:: console
Format:
(.venv) $ python3 basic.py <inputFile> <outputFile> <minSup> <maxPer>
Example Usage:
(.venv) $ python3 basic.py sampleTDB.txt patterns.txt 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.uncertainPeriodicFrequentPattern.basic import UPFPGrowth as alg
iFile = 'sampleDB.txt'
minSup = 10 # can also be specified between 0 and 1
maxPer = 2 # can also be specified between 0 and 1
obj = alg.UPFPGrowth(iFile, minSup, maxPer)
obj.mine()
periodicFrequentPatterns = obj.getPatterns()
print("Total number of Periodic Frequent Patterns:", len(periodicFrequentPatterns))
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.
"""
_rank = {}
_startTime = float()
_endTime = float()
_minSup = float()
_maxPer = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_lno = 0
_periodic = {}
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, _ab._pd.DataFrame):
uncertain, 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()
if 'uncertain' in i:
uncertain = self._iFile['uncertain'].tolist()
for k in range(len(data)):
tr = [ts[k]]
for j in range(len(k)):
product = _Item(data[k][j], uncertain[k][j])
tr.append(product)
self._Database.append(tr)
self._lno += 1
# 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 = [int(temp1[0])]
for i in range(len(temp1[1:])):
item = temp1[i]
probability = float(temp2[i])
product = _Item(item, probability)
tr.append(product)
self._lno += 1
self._Database.append(tr)
else:
try:
count = 0
with open(self._iFile, 'r') as f:
for line in f:
#count += 1
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 = [int(temp1[0])]
for i in range(len(temp1[1:])):
item = temp1[i]
probability = float(temp2[i])
product = _Item(item, probability)
tr.append(product)
self._lno += 1
self._Database.append(tr)
except IOError:
print("File Not Found")
def _periodicFrequentOneItem(self) -> Tuple[Dict, List]:
"""
Takes the transactions 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
:return: Tuple
"""
mapSupport = {}
for i in self._Database:
n = i[0]
for j in i[1:]:
if j.item not in mapSupport:
mapSupport[j.item] = [round(j.probability, 3), abs(0 - n), n]
else:
mapSupport[j.item][0] += round(j.probability, 3)
mapSupport[j.item][1] = max(mapSupport[j.item][1], abs(n - mapSupport[j.item][2]))
mapSupport[j.item][2] = n
for key in mapSupport:
mapSupport[key][1] = max(mapSupport[key][1], self._lno - mapSupport[key][2])
mapSupport = {k: [v[0], v[1]] for k, v in mapSupport.items() if v[1] <= self._maxPer and v[0] >= self._minSup}
plist = [k for k, v in sorted(mapSupport.items(), key=lambda x: (x[1][0], x[0]), reverse=True)]
self._rank = dict([(index, item) for (item, index) in enumerate(plist)])
return mapSupport, plist
def _check(self, i: List, x: List) -> 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 transactions
:type i : list
:return: value
:rtype: int
"""
for m in x:
k = 0
for n in i:
if m == n.item:
k += 1
if k == 0:
return 0
return 1
def _getPeriodAndSupport(self, s: float, timeStamps: List[int]) -> List[float]:
"""
To calculate periodicity of timeStamps
:param s: support of a pattern
:param timeStamps: timeStamps of a pattern
:return: periodicity and Support
"""
global __lno, _maxPer
timeStamps.sort()
cur = 0
per = 0
sup = s
for j in range(len(timeStamps)):
per = max(per, timeStamps[j] - cur)
if per > _maxPer:
return [0, 0]
cur = timeStamps[j]
per = max(per, _lno - cur)
return [sup, per]
def _buildTree(self, data: List[List], info: Dict) -> '_Tree':
"""
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 transaction 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.addTransactions(data[i][1:], set1)
return rootNode
def _updateTransactions(self, dict1: Dict) -> 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
:return: list
"""
list1 = []
for tr in self._Database:
list2 = [int(tr[0])]
for i in range(1, len(tr)):
if tr[i].item in dict1:
list2.append(tr[i])
if len(list2) >= 2:
basket = list2[1:]
basket.sort(key=lambda val: self._rank[val.item])
list2[1:] = basket[0:]
list1.append(list2)
return list1
def _convert(self, value: Union[int, float, str]) -> Union[int, 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 = float(value)
if type(value) is str:
if '.' in value:
value = float(value)
else:
value = int(value)
return value
def _removeFalsePositives(self) -> None:
"""
Removes the false positive patterns from the generated patterns.
This method iterates through the database to identify false positive patterns and removes them from the
generated patterns.
:return: None
"""
periods = {}
for i in self._Database:
for x, y in self._periodic.items():
if len(x) == 1:
periods[x] = y
else:
s = 1
check = self._check(i[1:], x)
if check == 1:
for j in i[1:]:
if j.item in x:
s *= j.probability
if x in periods:
periods[x][0] += s
else:
periods[x] = [s, y[1]]
for x, y in periods.items():
if y[0] >= _minSup:
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:
"""
Main method where the patterns are mined by constructing tree and remove the false patterns
by counting the original support of a patterns.
:return: None
"""
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.
:return: None
"""
global _lno, _maxPer, _minSup, _first, _last, periodic
self._startTime = _ab._time.time()
self._creatingItemSets()
self._finalPatterns = {}
self._minSup = self._convert(self._minSup)
self._maxPer = self._convert(self._maxPer)
_minSup, _maxPer, _lno = self._minSup, self._maxPer, self._lno
mapSupport, plist = self._periodicFrequentOneItem()
updatedTrans = self._updateTransactions(mapSupport)
info = {k: v for k, v in mapSupport.items()}
Tree1 = self._buildTree(updatedTrans, info)
self._periodic = {}
Tree1.generatePatterns([], self._periodic)
self._removeFalsePositives()
print("Periodic frequent patterns were generated successfully using UPFP 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) -> '_ab._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[0], b[1]])
dataframe = _ab._pd.DataFrame(data, columns=['Patterns', 'Support', 'Periodicity'])
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[0]) + ":" + str(y[1])
writer.write("%s \n" % s1)
[docs]
def getPatterns(self) -> Dict[str, List[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 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) == 5 or len(_ab._sys.argv) == 6:
if len(_ab._sys.argv) == 6:
_ap = UPFPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5])
if len(_ab._sys.argv) == 5:
_ap = UPFPGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
_ap.mine()
_ap.mine()
print("Total number of Uncertain 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")