# GPFPMiner is a Extension of ECLAT algorithm,which stands for Equivalence Class Clustering and bottom-up
# Lattice Traversal to mine the geo referenced peridoic frequent patterns.
#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
#
# import PAMI.geoReferencedPeridicFrequentPattern.GPFPMiner as alg
#
# obj = alg.GPFPMiner("sampleTDB.txt", "sampleN.txt", 5, 3)
#
# obj.mine()
#
# Patterns = obj.getPatterns()
#
# print("Total number of Geo Referenced Periodic-Frequent Patterns:", len(Patterns))
#
# obj.save("outFile")
#
# 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.geoReferencedPeriodicFrequentPattern.basic import abstract as _ab
from deprecated import deprecated
[docs]
class GPFPMiner(_ab._geoReferencedPeriodicFrequentPatterns):
"""
:Description: GPFPMiner is an Extension of ÉCLAT algorithm,which stands for Equivalence Class Clustering and
bottom-up Lattice Traversal to mine the geo referenced periodic frequent patterns.
:Reference:
:param iFile: str
Name of the Input file to mine complete set of Geo-referenced periodic frequent patterns
:param oFile: str
Name of the output file to store complete set of Geo-referenced periodic frequent patterns
:param 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.
:param maxPer: float
The user can specify maxPer 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.
:param nFile: str
Name of the input file to mine complete set of Geo-referenced periodic frequent patterns
: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 : str
Input file name or path of the input file
nFile : str
Name of Neighbourhood file name
minSup : float or int 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 : float or int 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 minSup 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.
startTime : float
To record the start time of the mining process
endTime : float
To record the completion time of the mining process
finalPatterns : dict
Storing the complete set of patterns in a dictionary variable
oFile : str
Name of the output file to store complete set of frequent patterns
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
Database : list
To store the complete set of transactions available in the input database/file
: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
getPatternsAsDataFrames()
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(iFileName)
Storing the complete transactions of the database/input file in a database variable
frequentOneItem()
Generating one frequent patterns
convert(value)
To convert the given user specified value
getNeighbourItems(keySet)
A function to get common neighbours of a itemSet
mapNeighbours(file)
A function to map items to their neighbours
**Executing the code on terminal :**
----------------------------------------
.. code-block:: console
Format:
(.venv) $ python3 GPFPMiner.py <inputFile> <outputFile> <neighbourFile> <minSup> <maxPer>
Example Usage:
(.venv) $ python3 GPFPMiner.py sampleTDB.txt output.txt sampleN.txt 0.5 0.3
.. note:: minSup & maxPer will be considered in percentage of database transactions
**Sample run of importing the code :**
-----------------------------------------
.. code-block:: python
import PAMI.geoReferencedPeridicFrequentPattern.GPFPMiner as alg
obj = alg.GPFPMiner("sampleTDB.txt", "sampleN.txt", 5, 3)
obj.mine()
Patterns = obj.getPatterns()
print("Total number of Geo Referenced Periodic-Frequent Patterns:", len(Patterns))
obj.save("outFile")
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.RaviKumar under the supervision of Professor Rage Uday Kiran.
"""
_minSup = " "
_maxPer = " "
_startTime = float()
_endTime = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_nFile = " "
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_sep = "\t"
_lno = 0
def __init__(self, iFile, nFile, minSup, maxPer, sep="\t"):
super().__init__(iFile, nFile, minSup, maxPer, sep)
self._NeighboursMap = {}
def _creatingItemSets(self):
"""
Storing the complete transactions of the database/input file in a database variable
"""
self._Database = []
if isinstance(self._iFile, _ab._pd.DataFrame):
data, ts = [], []
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'TS' in i:
ts = self._iFile['TS'].tolist()
if 'Transactions' in i:
data = self._iFile['Transactions'].tolist()
for i in range(len(data)):
tr = [ts[i][0]]
tr = tr + data[i]
self._Database.append(tr)
if isinstance(self._iFile, str):
if _ab._validators.url(self._iFile):
data = _ab._urlopen(self._iFile)
for line in data:
line.strip()
line = line.decode("utf-8")
temp = [i.rstrip() for i in line.split(self._sep)]
temp = [x for x in temp if x]
self._Database.append(temp)
else:
try:
with open(self._iFile, 'r', encoding='utf-8') as f:
for line in f:
line = line.rstrip()
temp = [i.strip() 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()
# function to get frequent one pattern
def _frequentOneItem(self):
"""
Generating one frequent patterns
"""
candidate = {}
for i in self._Database:
self._lno += 1
n = int(i[0])
for j in i[1:]:
if j not in candidate:
candidate[j] = [1, abs(0-n), n, [n]]
else:
candidate[j][0] += 1
candidate[j][1] = max(candidate[j][1], abs(n - candidate[j][2]))
candidate[j][2] = n
candidate[j][3].append(n)
self._minSup = self._convert(self._minSup)
self._maxPer = self._convert(self._maxPer)
#print(self._minSup, self._maxPer)
self._tidList = {k: v[3] for k, v in candidate.items() if v[0] >= self._minSup and v[1] <= self._maxPer}
candidate = {k: [v[0], v[1]] for k, v in candidate.items() if v[0] >= self._minSup and v[1] <= self._maxPer}
plist = [key for key, value in sorted(candidate.items(), key=lambda x: (x[1][0], x[0]), reverse=True)]
return plist
def _convert(self, value):
"""
To convert the given user specified value
:param value: user specified value
:type value: int or float or str
:return: converted value
:rtype: float
"""
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 _getSupportAndPeriod(self, timeStamps):
"""
calculates the support and periodicity with list of timestamps
:param timeStamps: timestamps of a pattern
:type timeStamps: list
"""
timeStamps.sort()
cur = 0
per = 0
sup = 0
for j in range(len(timeStamps)):
per = max(per, timeStamps[j] - cur)
if per > self._maxPer:
return [0, 0]
cur = timeStamps[j]
sup += 1
per = max(per, self._lno - cur)
return [sup, per]
def _save(self, prefix, suffix, tidSetX):
"""
Saves the patterns that satisfy the periodic frequent property.
:param prefix: the prefix of a pattern
:type prefix: list or None
:param suffix: the suffix of a patterns
:type suffix: list
:param tidSetX: the timestamp of a patterns
:type tidSetX: list
"""
if prefix is None:
prefix = suffix
else:
prefix = prefix + suffix
val = self._getSupportAndPeriod(tidSetX)
if val[0] >= self._minSup and val[1] <= self._maxPer:
self._finalPatterns[tuple(prefix)] = val
def _Generation(self, prefix, itemSets, tidSets):
"""
Generates the patterns that satisfy the periodic frequent property.
:param prefix: the prefix of a pattern
:type prefix: list or None
:param itemSets: the item sets of a patterns
:type itemSets: list
:param tidSets: the timestamp of a patterns
:type tidSets: list
"""
if len(itemSets) == 1:
i = itemSets[0]
tidI = tidSets[0]
self._save(prefix, [i], tidI)
return
for i in range(len(itemSets)):
itemX = itemSets[i]
if itemX is None:
continue
tidSetX = tidSets[i]
classItemSets = []
classTidSets = []
itemSetX = [itemX]
neighboursItemsI = self._getNeighbourItems(itemSets[i])
for j in range(i + 1, len(itemSets)):
neighboursItemsJ = self._getNeighbourItems(itemSets[i])
if not itemSets[j] in neighboursItemsI:
continue
itemJ = itemSets[j]
tidSetJ = tidSets[j]
y = list(set(tidSetX).intersection(tidSetJ))
if len(y) >= self._minSup:
ne = list(set(neighboursItemsI).intersection(neighboursItemsJ))
x = []
x = x + [itemX]
x = x + [itemJ]
self._NeighboursMap[tuple(x)] = ne
classItemSets.append(itemJ)
classTidSets.append(y)
newPrefix = list(set(itemSetX)) + prefix
self._Generation(newPrefix, classItemSets, classTidSets)
self._save(prefix, list(set(itemSetX)), tidSetX)
def _getNeighbourItems(self, keySet):
"""
A function to get Neighbours of a item
:param keySet: itemSet
:type keySet: str or tuple
:return: set of common neighbours
:rtype: set
"""
itemNeighbours = self._NeighboursMap.keys()
if isinstance(keySet, str):
if self._NeighboursMap.get(keySet) is None:
return []
itemNeighbours = list(set(itemNeighbours).intersection(set(self._NeighboursMap.get(keySet))))
if isinstance(keySet, tuple):
keySet = list(keySet)
for j in range(0, len(keySet)):
i = keySet[j]
itemNeighbours = list(set(itemNeighbours).intersection(set(self._NeighboursMap.get(i))))
return itemNeighbours
[docs]
def mapNeighbours(self):
"""
A function to map items to their Neighbours
"""
self._NeighboursMap = {}
if isinstance(self._nFile, _ab._pd.DataFrame):
data = []
if self._nFile.empty:
print("its empty..")
i = self._nFile.columns.values.tolist()
if 'Neighbours' in i:
data = self._nFile['Neighbours'].tolist()
for i in data:
self._NeighboursMap[i[0]] = i[1:]
if isinstance(self._nFile, str):
if _ab._validators.url(self._nFile):
data = _ab._urlopen(self._nFile)
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._NeighboursMap[temp[0]] = temp[1:]
else:
try:
with open(self._nFile, '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._NeighboursMap[temp[0]] = temp[1:]
except IOError:
print("File Not Found")
quit()
[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):
"""
Frequent pattern mining process will start from here
"""
self.mine()
[docs]
def mine(self):
"""
Frequent pattern mining process will start from here
"""
# global items_sets, endTime, startTime
self._startTime = _ab._time.time()
if self._iFile is None:
raise Exception("Please enter the file path or file name:")
self._creatingItemSets()
self._minSup = self._convert(self._minSup)
self.mapNeighbours()
self._finalPatterns = {}
plist = self._frequentOneItem()
for i in range(len(plist)):
itemX = plist[i]
tidSetX = self._tidList[itemX]
itemSetX = [itemX]
itemSets = []
tidSets = []
neighboursItems = self._getNeighbourItems(plist[i])
for j in range(i + 1, len(plist)):
if not plist[j] in neighboursItems:
continue
itemJ = plist[j]
tidSetJ = self._tidList[itemJ]
y1 = list(set(tidSetX).intersection(tidSetJ))
if len(y1) >= self._minSup:
itemSets.append(itemJ)
tidSets.append(y1)
self._Generation(itemSetX, itemSets, tidSets)
self._save(None, itemSetX, tidSetX)
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("Spatial Periodic Frequent patterns were generated successfully using SpatialEclat algorithm")
[docs]
def getMemoryUSS(self):
"""
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):
"""
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():
pat = ""
for i in a:
pat += str(i) + "\t"
data.append([pat, b[0], b[1]])
dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support', 'Period'])
return dataFrame
[docs]
def save(self, outFile):
"""
Complete set of frequent patterns will be loaded in to a 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():
pat = ""
for i in x:
pat += str(i) + "\t"
patternsAndSupport = pat + ": " + str(y[0]) + ": " + str(y[1])
writer.write("%s \n" % patternsAndSupport)
[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):
"""
This function is used to print the results
"""
print("Total number of Spatial Periodic-Frequent Patterns:", len(self.getPatterns()))
print("Total Memory in USS:", self.getMemoryUSS())
print("Total Memory in RSS", self.getMemoryRSS())
print("Total ExecutionTime in seconds:", self.getRuntime())
if __name__ == "__main__":
_ap = str()
if len(_ab._sys.argv) == 6 or len(_ab._sys.argv) == 7:
if len(_ab._sys.argv) == 7:
_ap = GPFPMiner(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5], _ab._sys.argv[6])
if len(_ab._sys.argv) == 6:
_ap = GPFPMiner(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5])
_ap.mine()
_ap.mine()
print("Total number of Spatial 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 seconds:", _ap.getRuntime())
else:
print("Error! The number of input parameters do not match the total number of parameters provided")