# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
# from PAMI.periodicFrequentPattern.maximal import MaxPFGrowth as alg
#
# obj = alg.MaxPFGrowth("../basic/sampleTDB.txt", "2", "6")
#
# obj.mine()
#
# Patterns = obj.getPatterns()
#
# print("Total number of Frequent Patterns:", len(Patterns))
#
# obj.save("patterns")
#
# 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
"""
from PAMI.periodicFrequentPattern.maximal import abstract as _ab
from typing import List, Dict, Tuple, Set, Union, Any, Generator
from PAMI.periodicFrequentPattern.basic import abstract as _ab
import pandas as pd
from deprecated import deprecated
#global maximalTree
_minSup = float()
_maxPer = float()
_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 Database 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:
"""
To add the children details to the parent node children list
:param node: children node
:return: adding to parent node children
"""
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(Database)
creating Database as a branch in frequentPatternTree
getConditionPatterns(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 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 = {}
#self.maximalTree = _MPTree()
def addTransaction(self, transaction: List[Any], tid: List[int]) -> None:
"""
adding transaction into database
:param transaction: transactions in a database
:param tid: timestamp of the transaction in database
:return: pftree
"""
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: Any) -> Tuple[List[List[Any]], List[List[int]], Dict[Any, List[int]]]:
"""
to get the conditional patterns of a node
:param alpha: node in the tree
:return: conditional patterns of a 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 = _conditionalTransactions(finalPatterns, finalSets)
return finalPatterns, finalSets, info
def removeNode(self, nodeValue: Any) -> None:
"""
removes the leaf node by pushing its timestamps to parent node
:param nodeValue: node of a tree
:return: None
"""
for _i in self.summaries[nodeValue]:
_i.parent.timeStamps = _i.parent.timeStamps + _i.timeStamps
del _i.parent.children[nodeValue]
_i = None
def getTimeStamps(self, alpha: Any) -> List[int]:
"""
to get all the timestamps related to a node in tree
:param alpha: node of a tree
:return: timestamps of a node
"""
temp = []
for j in self.summaries[alpha]:
temp += j.timeStamps
return temp
def generatePatterns(self, prefix: List[Any], patterns: Dict[Tuple[Any], Tuple[int, int]], maximalTree: Any) -> None:
"""
To generate the maximal periodic frequent patterns
:param prefix: an empty list of itemSet to form the combinations
:param patterns: dictionary of patterns
:param maximalTree: maximal Tree
:return: maximal periodic frequent patterns
"""
#global maximalTree
for _i__ in sorted(self.summaries, key=lambda x: (self.info.get(x), -x)):
pattern = prefix[:]
pattern.append(_i__)
condPattern, timeStamps, info = self.getConditionalPatterns(_i__)
conditionalTree = _Tree()
conditionalTree.info = info.copy()
head = pattern[:]
tail = []
for k in info:
tail.append(k)
sub = head + tail
if maximalTree.checkerSub(sub) == 1:
for pat in range(len(condPattern)):
conditionalTree.addTransaction(condPattern[pat], timeStamps[pat])
if len(condPattern) >= 1:
conditionalTree.generatePatterns(pattern, patterns, maximalTree)
else:
maximalTree.addTransaction(pattern)
patterns[tuple(pattern)] = self.info[_i__]
self.removeNode(_i__)
class _MNode(object):
"""
A class used to represent the node of frequentPatternTree
:Attributes:
item : int
storing item of a node
children : list
To maintain the children of node
:Methods:
addChild(itemName)
storing the children to their respective parent nodes
"""
def __init__(self, item: Any, children: Dict[Any, Any]) -> None:
self.item = item
self.children = children
def addChild(self, node: Any) -> None:
"""
To add the children details to parent node children variable
:param node: children node
:return: adding children node to parent node
"""
self.children[node.item] = node
node.parent = self
class _MPTree(object):
"""
A class used to represent the node of frequentPatternTree
:Attributes:
root : node
the root of a tree
summaries : dict
to store the items with same name into dictionary
:Methods:
addTransaction(itemSet)
the generated periodic-frequent pattern is added into maximal-tree
checkerSub(itemSet)
to check of subset of itemSet is present in tree
"""
def __init__(self) -> None:
self.root = _MNode(None, {})
self.summaries = {}
def addTransaction(self, transaction: List[Any]) -> None:
"""
to add the transaction in maximal tree
:param transaction: resultant periodic frequent pattern
:return: maximal tree
"""
currentNode = self.root
transaction.sort()
for k in range(len(transaction)):
if transaction[k] not in currentNode.children:
newNode = _MNode(transaction[k], {})
currentNode.addChild(newNode)
if transaction[k] in self.summaries:
self.summaries[transaction[k]].insert(0, newNode)
else:
self.summaries[transaction[k]] = [newNode]
currentNode = newNode
else:
currentNode = currentNode.children[transaction[k]]
def checkerSub(self, items: List[Any]) -> int:
"""
To check subset present of items in the maximal tree
:param items: the pattern to check for subsets
:return: 1
"""
items.sort(reverse=True)
item = items[0]
if item not in self.summaries:
return 1
else:
if len(items) == 1:
return 0
for t in self.summaries[item]:
cur = t.parent
k_ = 1
while cur.item is not None:
if items[k_] == cur.item:
k_ += 1
if k_ == len(items):
return 0
cur = cur.parent
return 1
#maximalTree = _MPTree()
def _getPeriodAndSupport(timeStamps: List[int]) -> List[Union[int, float]]:
"""
To calculate the periodicity and support of a pattern with their respective timeStamps
:param timeStamps: timeStamps
:return: Support and periodicity
"""
timeStamps.sort()
cur = 0
per = 0
sup = 0
for j in range(len(timeStamps)):
per = max(per, timeStamps[j] - cur)
if per > _maxPer:
return [0, 0]
cur = timeStamps[j]
sup += 1
per = max(per, abs(_lno - cur))
return [sup, per]
def _conditionalTransactions(condPatterns: List[List[int]], condTimeStamps: List[List[int]]) -> Tuple[List[List[int]], List[List[int]], Dict[int, Tuple[int, float]]]:
"""
To calculate the timestamps of conditional items in conditional patterns
:param condPatterns: conditional patterns of node
:param condTimeStamps: timeStamps of a conditional patterns
:return: removing items with low minSup or periodicity and sort the conditional transactions
"""
pat = []
timeStamps = []
data1 = {}
for cond in range(len(condPatterns)):
for j in condPatterns[cond]:
if j in data1:
data1[j] = data1[j] + condTimeStamps[cond]
else:
data1[j] = condTimeStamps[cond]
updatedDict = {}
for m in data1:
updatedDict[m] = _getPeriodAndSupport(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], -x), reverse=True)
if len(trans) > 0:
pat.append(trans)
timeStamps.append(condTimeStamps[count])
count += 1
return pat, timeStamps, updatedDict
[docs]
class MaxPFGrowth(_ab._periodicFrequentPatterns):
"""
:Description: MaxPF-Growth is one of the fundamental algorithm to discover maximal periodic-frequent
patterns in a temporal database.
:Reference: R. Uday Kiran, Yutaka Watanobe, Bhaskar Chaudhury, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa,
"Discovering Maximal Periodic-Frequent Patterns in Very Large Temporal Databases",
IEEE 2020, https://ieeexplore.ieee.org/document/9260063
:param iFile: str :
Name of the Input file to mine complete set of periodic frequent pattern's
:param oFile: str :
Name of the output file to store complete set of periodic frequent pattern's
:param minSup: str:
Controls the minimum number of transactions in which every item must appear in a database.
:param maxPer: float:
Controls the maximum number of transactions in which any two items within a pattern can reappear.
:param sep: str :
This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.
:Attributes:
iFile : file
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
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 separator 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
it represents the total no of transaction
tree : class
it represents the Tree class
itemSetCount : int
it represents the total no of patterns
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 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 or dataframes and stores in list format
PeriodicFrequentOneItem()
Extracts the one-periodic-frequent patterns from Databases
updateDatabases()
update the Databases by removing aperiodic items and sort the Database by item decreased support
buildTree()
after updating the Databases ar added into the tree by setting root node as null
mine()
the main method to run the program
**Executing the code on terminal:**
-------------------------------------
.. code-block:: console
Format:
(.venv) $ python3 maxpfrowth.py <inputFile> <outputFile> <minSup> <maxPer>
Examples usage :
(.venv) $ python3 maxpfrowth.py sampleTDB.txt patterns.txt 0.3 0.4
.. note:: minSup will be considered in percentage of database transactions
**Sample run of the imported code:**
------------------------------------------
.. code-block:: python
from PAMI.periodicFrequentPattern.maximal import MaxPFGrowth as alg
obj = alg.MaxPFGrowth("../basic/sampleTDB.txt", "2", "6")
obj.mine()
Patterns = obj.getPatterns()
print("Total number of Frequent Patterns:", len(Patterns))
obj.save("patterns")
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()
_maxPer = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_rank = {}
_rankedUp = {}
_lno = 0
_patterns = {}
_maximalTree = str()
def __init__(self, iFile: Any, minSup: Union[int, float, str], maxPer: Union[int, float, str], sep: str='\t') -> None:
super().__init__(iFile, minSup, maxPer, sep)
def _creatingItemSets(self) -> None:
"""
Storing the complete Databases of the database/input file in a database variable
:rtype: storing transactions into Database variable
:return: None
"""
self._Database = []
if isinstance(self._iFile, _ab._pd.DataFrame):
data, ts = [], []
if self._iFile.empty:
print("its empty..")
itemList = self._iFile.columns.values.tolist()
if 'TS' in itemList:
ts = self._iFile['TS'].tolist()
if 'Transactions' in itemList:
data = self._iFile['Transactions'].tolist()
for num in range(len(data)):
tr = [ts[num][0]] + data[num]
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 = [item.rstrip() for item 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 = [item_.rstrip() for item_ 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) -> Dict[Any, List[Union[int, float]]]:
"""
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
:rtype: return the one-length periodic frequent patterns
"""
data = {}
for tr in self._Database:
for tr1 in range(1, len(tr)):
if tr[tr1] not in data:
data[tr[tr1]] = [int(tr[0]), int(tr[0]), 1]
else:
data[tr[tr1]][0] = max(data[tr[tr1]][0], (int(tr[0]) - data[tr[tr1]][1]))
data[tr[tr1]][1] = int(tr[0])
data[tr[tr1]][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]] 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)])
return data
def _updateDatabases(self, dict1: Dict[Any, List[Union[int, float]]]) -> List[List[Union[int, float]]]:
"""
Remove the items which are not frequent from Databases and updates the Databases with rank of items
:param dict1: frequent items with support
:type dict1: dictionary
:rtype: sorted and updated transactions
"""
list1 = []
for tr in self._Database:
list2 = [int(tr[0])]
for ij in range(1, len(tr)):
if tr[ij] in dict1:
list2.append(self._rank[tr[ij]])
if len(list2) >= 2:
basket = list2[1:]
basket.sort()
list2[1:] = basket[0:]
list1.append(list2)
return list1
@staticmethod
def _buildTree(data: List[List[Union[int, float]]], info: Dict[Any, List[Union[int, float]]]) -> Any:
"""
it takes the Databases and support of each item and construct the main tree with setting root node as null
:param data: it represents the one Databases in database
:type data: list
:param info: it represents the support of each item
:type info: dictionary
:rtype: returns root node of tree
"""
rootNode = _Tree()
rootNode.info = info.copy()
for num_ in range(len(data)):
set1 = [data[num_][0]]
rootNode.addTransaction(data[num_][1:], set1)
return rootNode
def _savePeriodic(self, itemSet: List[Any]) -> List[Any]:
"""
To convert the ranks of items in to their original item names
:param itemSet: frequent pattern.
:return: frequent pattern with original item names
"""
t1 = []
for _ in itemSet:
t1.append(self._rankedUp[_])
return t1
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 = (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]
@deprecated("It is recommended to use mine() instead of mine() for mining process")
def startMine(self) -> None:
"""
Mining process will start from this function
:return: None
"""
global _minSup, _maxPer, _lno
self._patterns = {}
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._maxPer = self._convert(self._maxPer)
_minSup, _maxPer, _lno = self._minSup, self._maxPer, len(self._Database)
if self._minSup > len(self._Database):
raise Exception("Please enter the minSup in range between 0 to 1")
_generatedItems = 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)
self._finalPatterns = {}
self._maximalTree = _MPTree()
_Tree_.generatePatterns([], self._patterns, self._maximalTree)
for x, y in self._patterns.items():
pattern = str()
x = self._savePeriodic(x)
for i__ in x:
pattern = pattern + i__ + " "
self._finalPatterns[pattern] = y
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("Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth algorithm ")
[docs]
def mine(self) -> None:
"""
Mining process will start from this function
:return: None
"""
global _minSup, _maxPer, _lno
self._patterns = {}
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._maxPer = self._convert(self._maxPer)
_minSup, _maxPer, _lno = self._minSup, self._maxPer, len(self._Database)
if self._minSup > len(self._Database):
raise Exception("Please enter the minSup in range between 0 to 1")
_generatedItems = 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)
self._finalPatterns = {}
self._maximalTree = _MPTree()
__Tree.generatePatterns([], self._patterns, self._maximalTree)
for x, y in self._patterns.items():
pattern = str()
x = self._savePeriodic(x)
for _i_ in x:
pattern = pattern + _i_ + " "
self._finalPatterns[pattern] = y
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("Maximal Periodic Frequent patterns were generated successfully using MAX-PFPGrowth 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) -> _ab._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, 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 periodic-frequent patterns will be loaded in to a 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.replace(' ', '\t').strip() + ":" + str(y[0]) + ":" + str(y[1])
writer.write("%s \n" % s1)
[docs]
def getPatterns(self) -> Dict[str, Tuple[int, int]]:
"""
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:
print("Total number of Maximal 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 = MaxPFGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5])
if len(_ab._sys.argv) == 5:
_ap = MaxPFGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
_ap.mine()
print("Total number of Maximal 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:
for i in [100, 200, 300, 400, 500]:
_ap = MaxPFGrowth('/Users/Likhitha/Downloads/temporal_T10I4D100K.csv', i, 5000, '\t')
_ap.mine()
print("Total number of Maximal Partial Periodic Patterns:", len(_ap.getPatterns()))
_ap.save('/Users/Likhitha/Downloads/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")