# PS-Growth is one of the fundamental algorithm to discover periodic-frequent patterns in a temporal database.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
#
# from PAMI.periodicFrequentPattern.basic import PSGrowth as alg
#
# obj = alg.PSGrowth("../basic/sampleTDB.txt", "2", "6")
#
# obj.mine()
#
# periodicFrequentPatterns = obj.getPatterns()
#
# print("Total number of Patterns:", len(periodicFrequentPatterns))
#
# 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.basic import abstract as _ab
import pandas as pd
from deprecated import deprecated
from itertools import combinations as _combinations
from PAMI.periodicFrequentPattern.basic import abstract as _ab
from typing import List, Dict, Tuple, Set, Union, Any, Generator
_pfList = []
_minSup = int()
_maxPer = int()
_lno = int()
class _Interval(object):
"""
To represent the timestamp interval of a node in summaries
"""
def __init__(self, start, end, per, sup) -> None:
self.start = start
self.end = end
self.per = per
self.sup = sup
class _NodeSummaries(object):
"""
To define the summaries of timeStamps of a node
:Attributes:
totalSummaries : list
stores the summaries of timestamps
:Methods:
insert(timeStamps)
inserting and merging the timestamps into the summaries of a node
"""
def __init__(self) -> None:
self.totalSummaries = []
def insert(self, tid) -> List[_Interval]:
""" To insert and merge the timeStamps into summaries of a node
:param tid: timeStamps of a node
:return: summaries of a node
"""
k = self.totalSummaries[-1]
diff = tid - k.end
if diff <= _maxPer:
k.end = tid
k.per = max(diff, k.per)
# print(k.per)
k.sup += 1
else:
self.totalSummaries.append(_Interval(tid, tid, 0, 1))
return self.totalSummaries
def _merge(summariesX, summariesY) -> List[_Interval]:
"""
To Merge the timeStamps
:param summariesX: TimeStamps of a one itemSet
:param summariesY: TimeStamps of a one itemSet
:return: Merged timestamp of both itemSets
"""
iter1 = 0
iter2 = 0
updatedSummaries = []
l1 = len(summariesX)
l2 = len(summariesY)
while 1:
if summariesX[iter1].start < summariesY[iter2].start:
if summariesX[iter1].end < summariesY[iter2].start:
diff = summariesY[iter2].start - summariesX[iter1].end
if diff > _maxPer:
updatedSummaries.append(_Interval(summariesX[iter1].start,
summariesX[iter1].end, summariesX[iter1].per,
summariesX[iter1].sup))
iter1 += 1
if iter1 >= l1:
ck = 1
break
else:
per1 = max(diff, summariesX[iter1].per)
per1 = max(per1, summariesY[iter2].per)
updatedSummaries.append(
_Interval(summariesX[iter1].start, summariesY[iter2].end, per1,
summariesX[iter1].sup + summariesY[iter2].sup))
iter1 += 1
iter2 += 1
if iter1 >= l1:
ck = 1
break
if iter2 >= l2:
ck = 2
break
else:
if summariesX[iter1].end > summariesY[iter2].end:
updatedSummaries.append(_Interval(summariesX[iter1].start, summariesX[iter1].end,
summariesX[iter1].per,
summariesX[iter1].sup + summariesY[iter2].sup))
else:
per1 = max(summariesX[iter1].per, summariesY[iter2].per)
updatedSummaries.append(
_Interval(summariesX[iter1].start, summariesY[iter2].end, per1,
summariesX[iter1].sup + summariesY[iter2].sup))
iter1 += 1
iter2 += 1
if iter1 >= l1:
ck = 1
break
if iter2 >= l2:
ck = 2
break
else:
if summariesY[iter2].end < summariesX[iter1].start:
diff = summariesX[iter1].start - summariesY[iter2].end
if diff > _maxPer:
updatedSummaries.append(_Interval(summariesY[iter2].start, summariesY[iter2].end,
summariesY[iter2].per, summariesY[iter2].sup))
iter2 += 1
if iter2 >= l2:
ck = 2
break
else:
per1 = max(diff, summariesY[iter2].per)
per1 = max(per1, summariesX[iter1].per)
updatedSummaries.append(
_Interval(summariesY[iter2].start, summariesX[iter1].end, per1,
summariesY[iter2].sup + summariesX[iter1].sup))
iter2 += 1
iter1 += 1
if iter2 >= l2:
ck = 2
break
if iter1 >= l1:
ck = 1
break
else:
if summariesY[iter2].end > summariesX[iter1].end:
updatedSummaries.append(_Interval(summariesY[iter2].start, summariesY[iter2].end,
summariesY[iter2].per,
summariesY[iter2].sup + summariesX[iter1].sup))
else:
per1 = max(summariesY[iter2].per, summariesX[iter1].per)
updatedSummaries.append(
_Interval(summariesY[iter2].start, summariesX[iter1].end, per1,
summariesY[iter2].sup + summariesX[iter1].sup))
iter2 += 1
iter1 += 1
if iter2 >= l2:
ck = 2
break
if iter1 >= l1:
ck = 1
break
if ck == 1:
while iter2 < l2:
updatedSummaries.append(summariesY[iter2])
iter2 += 1
else:
while iter1 < l1:
updatedSummaries.append(summariesX[iter1])
iter1 += 1
updatedSummaries = _update(updatedSummaries)
return updatedSummaries
def _update(updatedSummaries) -> List[_Interval]:
""" After updating the summaries with first, last, and period elements in summaries
:param updatedSummaries: summaries that have been merged
:return: updated summaries of a node
"""
summaries = [updatedSummaries[0]]
cur = updatedSummaries[0]
for i in range(1, len(updatedSummaries)):
v = (updatedSummaries[i].start - cur.end)
if cur.end > updatedSummaries[i].start or v <= _maxPer:
cur.end = max(updatedSummaries[i].end, cur.end)
cur.sup += updatedSummaries[i].sup
cur.per = max(cur.per, updatedSummaries[i].per)
cur.per = max(cur.per, v)
else:
summaries.append(updatedSummaries[i])
cur = summaries[-1]
return summaries
[docs]
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, children) -> None:
"""
Initializing the Node class
:param item: Storing the item of a node
:type item: int
:param children: To maintain the children of a node
:type children: dict
:return: None
"""
self.item = item
self.children = children
self.parent = None
self.timeStamps = _NodeSummaries()
[docs]
def addChild(self, node) -> None:
"""
Appends the children node details to a parent node
:param node: children node
:return: appending children node to parent node
"""
self.children[node.item] = node
node.parent = self
class _Tree(object):
"""
A class used to represent the frequentPatternGrowth tree structure
:Attributes:
root : Node or None
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
addConditionalTransactions(prefixPaths, supportOfItems)
construct the conditional tree for prefix paths
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 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 periodic-frequent patterns
"""
def __init__(self) -> None:
self.root = Node(None, {})
self.summaries = {}
self.info = {}
def addTransaction(self, transaction, tid) -> None:
"""
Adding transaction into the tree
:param transaction: it represents the one transaction in a database
:type transaction: list
:param tid: represents the timestamp of a transaction
:type tid: list
:return: None
"""
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]]
if len(currentNode.timeStamps.totalSummaries) != 0:
currentNode.timeStamps.insert(tid)
else:
currentNode.timeStamps.totalSummaries.append(_Interval(tid, tid, 0, 1))
def addConditionalPatterns(self, transaction, tid) -> None:
"""
To add the conditional transactions in to conditional tree
:param transaction: conditional transaction list of a node
:param tid: timestamp of a conditional transaction
:return: the conditional tree of a node
"""
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]]
if len(currentNode.timeStamps.totalSummaries) != 0:
currentNode.timeStamps.totalSummaries = _merge(currentNode.timeStamps.totalSummaries, tid)
else:
currentNode.timeStamps.totalSummaries = tid
def getConditionalPatterns(self, alpha) -> Tuple[List[List[int]], List[List[_Interval]], Dict[int, Tuple[int, int]]]:
"""
To mine the conditional patterns of a node
:param alpha: starts from the leaf node of a tree
:return: the conditional patterns of a node
"""
finalPatterns = []
finalSets = []
for i in self.summaries[alpha]:
set1 = i.timeStamps.totalSummaries
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) -> None:
"""
to remove the node from the tree by pushing the timeStamps of leaf node to the parent node
:param nodeValue: name of node to be deleted
:return: removes the node from the tree
"""
for i in self.summaries[nodeValue]:
if len(i.parent.timeStamps.totalSummaries) != 0:
i.parent.timeStamps.totalSummaries = _merge(i.parent.timeStamps.totalSummaries,
i.timeStamps.totalSummaries)
else:
i.parent.timeStamps.totalSummaries = i.timeStamps.totalSummaries
del i.parent.children[nodeValue]
del i
del self.summaries[nodeValue]
def getTimeStamps(self, alpha) -> List[_Interval]:
"""
To get the timeStamps of a respective node
:param alpha: name of node for the timeStamp
:return: timeStamps of a node
"""
temp = []
for i in self.summaries[alpha]:
temp += i.timeStamps
return temp
def check(self) -> int:
"""
To the total number of child and their summaries
:return: int
"""
k = self.root
while len(k.children) != 0:
if len(k.children) > 1:
return 1
if len(k.children) != 0 and len(k.timeStamps.totalSummaries) > 0:
return 1
for j in k.children:
v = k.children[j]
k = v
return -1
def generatePatterns(self, prefix):
"""
Generating the patterns from the tree
:param prefix: empty list to form the combinations
:return: returning the periodic-frequent patterns from the tree
"""
global _pfList
for i in sorted(self.summaries, key=lambda x: (self.info.get(x)[0], -x)):
pattern = prefix[:]
pattern.append(_pfList[i])
yield pattern, self.info[i]
patterns, timeStamps, info = self.getConditionalPatterns(i)
conditionalTree = _Tree()
conditionalTree.info = info.copy()
for pat in range(len(patterns)):
conditionalTree.addConditionalPatterns(patterns[pat], timeStamps[pat])
find = conditionalTree.check()
if find == 1:
del patterns, timeStamps, info
for cp in conditionalTree.generatePatterns(pattern):
yield cp
else:
if len(conditionalTree.info) != 0:
j = []
for r in timeStamps:
j += r
inf = getPeriodAndSupport(j)
patterns[0].reverse()
upp = []
for jm in patterns[0]:
upp.append(_pfList[jm])
allSubsets = _subLists(upp)
# print(upp,inf)
for pa in allSubsets:
yield pattern + pa, inf
del patterns, timeStamps, info
del conditionalTree
self.removeNode(i)
def _subLists(itemSet) -> List[List[int]]:
"""
Forms all the subsets of given itemSet
:param itemSet: itemSet or a list of periodic-frequent items
:return: subsets of itemSet
"""
subs = []
for i in range(1, len(itemSet) + 1):
temp = [list(x) for x in _combinations(itemSet, i)]
if len(temp) > 0:
subs.extend(temp)
return subs
[docs]
def getPeriodAndSupport(timeStamps) -> List[int]:
"""
Calculates the period and support of list of timeStamps
:param timeStamps: timeStamps of a pattern or item
:return: support and periodicity
"""
cur = 0
per = 0
sup = 0
for j in range(len(timeStamps)):
per = max(per, timeStamps[j].start - cur)
per = max(per, timeStamps[j].per)
if per > _maxPer:
return [0, 0]
cur = timeStamps[j].end
sup += timeStamps[j].sup
per = max(per, _lno - cur)
return [sup, per]
[docs]
def conditionalTransactions(patterns, timestamp) -> Tuple[List[List[int]], List[List[_Interval]], Dict[int, Tuple[int, int]]]:
"""
To sort and update the conditional transactions by removing the items which fails frequency
and periodicity conditions
:param patterns: conditional patterns of a node
:param timestamp: timeStamps of a conditional pattern
:return: conditional transactions with their respective timeStamps
"""
global _minSup, _maxPer
pat = []
timeStamps = []
data1 = {}
for i in range(len(patterns)):
for j in patterns[i]:
if j in data1:
data1[j] = _merge(data1[j], timestamp[i])
else:
data1[j] = timestamp[i]
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 patterns:
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(timestamp[count])
count += 1
return pat, timeStamps, updatedDict
[docs]
class PSGrowth(_ab._periodicFrequentPatterns):
"""
:Description: PS-Growth is one of the fundamental algorithm to discover periodic-frequent patterns in a temporal database.
:Reference : A. Anirudh, R. U. Kiran, P. K. Reddy and M. Kitsuregaway, "Memory efficient mining of periodic-frequent
patterns in transactional databases," 2016 IEEE Symposium Series on Computational Intelligence (SSCI),
2016, pp. 1-8, https://doi.org/10.1109/SSCI.2016.7849926
: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: str:
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 an output file
getConditionalPatternsInDataFrame()
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
OneLengthItems()
Scans the dataset or dataframes and stores in list format
buildTree()
after updating the Databases ar added into the tree by setting root node as null
**Methods to execute code on terminal**
-----------------------------------------
.. code-block:: console
Format:
(.venv) $ python3 PSGrowth.py <inputFile> <outputFile> <minSup> <maxPer>
Example:
(.venv) $ python3 PSGrowth.py sampleTDB.txt patterns.txt 0.3 0.4
.. note:: minSup will be considered in percentage of database transactions
**Importing this algorithm into a python program**
----------------------------------------------------
.. code-block:: python
from PAMI.periodicFrequentPattern.basic import PSGrowth as alg
obj = alg.PSGrowth("../basic/sampleTDB.txt", "2", "6")
obj.mine()
periodicFrequentPatterns = obj.getPatterns()
print("Total number of Patterns:", len(periodicFrequentPatterns))
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.
"""
_startTime = float()
_endTime = float()
_minSup = str()
_maxPer = str()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_rank = {}
_lno = 0
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
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):
ts, data = [], []
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)):
if data[i]:
tr = [str(ts[i])] + [x for x in data[i].split(self._sep)]
self._Database.append(tr)
else:
self._Database.append([str(ts[i])])
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 _OneLengthItems(self):
"""
Storing the complete values of a database/input file into a database variable
"""
data = {}
global _minSup, _maxPer, _lno
for tr in self._Database:
self._lno += 1
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], self._lno - data[key][1])
self._minSup = self._convert(self._minSup)
self._maxPer = self._convert(self._maxPer)
_minSup, _maxPer, _lno = self._minSup, self._maxPer, self._lno
data = {k: [v[2], v[0]] for k, v in data.items() if v[0] <= self._maxPer and v[2] >= self._minSup}
genList = [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(genList)])
return data, genList
def _buildTree(self, info, sampleDict) -> _Tree:
"""
it takes the Databases and support of each item and construct the main tree with setting root node as null
:param info: it represents the support of each item
:type info: dictionary
:param sampleDict: One length periodic-frequent patterns in a dictionary
:type sampleDict: dict
:return: Returns the root node of the tree
"""
rootNode = _Tree()
rootNode.info = info.copy()
k = 0
for line in self._Database:
k += 1
tr = line
list2 = [int(tr[0])]
for i in range(1, len(tr)):
if tr[i] in sampleDict:
list2.append(self._rank[tr[i]])
if len(list2) >= 2:
basket = list2[1:]
basket.sort()
list2[1:] = basket[0:]
rootNode.addTransaction(list2[1:], list2[0])
return rootNode
[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, _pfList
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()
OneLengthPeriodicItems, _pfList = self._OneLengthItems()
info = {self._rank[k]: v for k, v in OneLengthPeriodicItems.items()}
Tree = self._buildTree(info, OneLengthPeriodicItems)
patterns = Tree.generatePatterns([])
self._finalPatterns = {}
for i in patterns:
sample = str()
for k in i[0]:
sample = sample + k + "\t"
self._finalPatterns[sample] = i[1]
self._endTime = _ab._time.time()
self._memoryUSS = float()
self._memoryRSS = float()
process = _ab._psutil.Process(_ab._os.getpid())
self._memoryUSS = process.memory_full_info().uss
self._memoryRSS = process.memory_info().rss
print("Periodic-Frequent patterns were generated successfully using PS-Growth algorithm ")
[docs]
def mine(self) -> None:
"""
Mining process will start from this function
:return: None
"""
global _minSup, _maxPer, _lno, _pfList
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()
OneLengthPeriodicItems, _pfList = self._OneLengthItems()
info = {self._rank[k]: v for k, v in OneLengthPeriodicItems.items()}
Tree = self._buildTree(info, OneLengthPeriodicItems)
patterns = Tree.generatePatterns([])
self._finalPatterns = {}
for i in patterns:
sample = str()
for k in i[0]:
sample = sample + k + "\t"
self._finalPatterns[sample] = i[1]
self._endTime = _ab._time.time()
self._memoryUSS = float()
self._memoryRSS = float()
process = _ab._psutil.Process(_ab._os.getpid())
self._memoryUSS = process.memory_full_info().uss
self._memoryRSS = process.memory_info().rss
print("Periodic-Frequent patterns were generated successfully using PS-Growth 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 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 + ":" + str(y[0]) + ":" + str(y[1])
#s1 = x.replace(' ', '\t').strip() + ":" + str(y[0]) + ":" + str(y[1])
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 the results
:return: None
"""
print("Total number of 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 = PSGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5])
if len(_ab._sys.argv) == 5:
_ap = PSGrowth(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
_ap.mine()
print("Total number of 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")