#
# **Importing this algorithm into a python program**
# --------------------------------------------------------
#
# from PAMI.fuzzyGeoreferencedPeriodicFrequentPattern import FGPFPMiner as alg
#
# obj = alg.FFSPMiner("input.txt", "neighbours.txt", 3, 4)
#
# obj.mine()
#
# print("Total number of fuzzy frequent spatial patterns:", len(obj.getPatterns()))
#
# obj.save("outputFile")
#
# print("Total Memory in USS:", obj.getMemoryUSS())
#
# print("Total Memory in RSS", obj.getMemoryRSS())
#
# print("Total ExecutionTime in seconds:", obj.getRuntime())
#
__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
"""
import pandas as pd
import plotly.express as px
import PAMI.fuzzyGeoreferencedPeriodicFrequentPattern.basic.abstract as _ab
from deprecated import deprecated
class _FFList:
"""
A class represent a Fuzzy List of an element
:Attributes:
item : int
the item name
sumIUtil : float
the sum of utilities of a fuzzy item in database
sumRUtil : float
the sum of resting values of a fuzzy item in database
elements : list
a list of elements contain tid,Utility and resting values of element in each transaction
:Methods:
addElement(element)
Method to add an element to this fuzzy list and update the sums at the same time.
printElement(e)
Method to print elements
"""
def __init__(self, itemName):
self.item = itemName
self.isPeriodic = False
self.sumIUtil = 0.0
self.sumRUtil = 0.0
self.elements = []
def addElement(self, element):
"""
A Method that add a new element to FFList
:param element: an element to be added to FFList
:param element: Element
"""
self.sumIUtil += element.iUtils
self.sumRUtil += element.rUtils
self.elements.append(element)
def printElement(self):
"""
A Method to Print elements in the FFList object
"""
for ele in self.elements:
print(ele.tid, ele.iUtils, ele.rUtils)
class _Element:
"""
A class represents an Element of a fuzzy list
:Attributes:
tid : int
keep tact of transaction id
iUtils : float
the utility of a fuzzy item in the transaction
rUtils : float
the neighbourhood resting value of a fuzzy item in the transaction
"""
def __init__(self, tid, iUtil, rUtil):
self.tid = tid
self.iUtils = iUtil
self.rUtils = rUtil
class _Pair:
"""
A class to store item and it's quantity together
"""
def __init__(self):
self.item = 0
self.quantity = 0
[docs]
class FGPFPMiner(_ab._fuzzySpatialFrequentPatterns):
"""
:Description: Fuzzy Frequent Spatial Pattern-Miner is desired to find all Spatially frequent fuzzy patterns
which is on-trivial and challenging problem to its huge search space.we are using efficient pruning
techniques to reduce the search space.
:Reference:
:param iFile: str :
Name of the Input file to mine complete set of frequent patterns
:param oFile: str :
Name of the output file to store complete set of 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 frequent patterns
:param FuzFile: str :
The user can specify fuzFile.
: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 to mine complete set of fuzzy spatial frequent patterns
oFile : file
Name of the oFile file to store complete set of fuzzy spatial frequent patterns
minSup : float
The user given minimum support
neighbors : map
keep track of neighbours of elements
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
itemsCnt : int
To record the number of fuzzy spatial itemSets generated
mapItemSum : map
To keep track of sum of Fuzzy Values of items
mapItemRegions : map
To Keep track of fuzzy regions of item
joinsCnt : int
To keep track of the number of FFI-list that was constructed
BufferSize : int
represent the size of Buffer
itemSetBuffer list
to keep track of items in buffer
:Methods:
mine()
Mining process will start from here
getPatterns()
Complete set of patterns will be retrieved with this function
save(oFile)
Complete set of frequent patterns will be loaded in to a output file
getPatternsAsDataFrame()
Complete set of frequent patterns will be loaded in to a dataframe
getMemoryUSS()
Total amount of USS memory consumed by the mining process will be retrieved from this function
getMemoryRSS()
Total amount of RSS memory consumed by the mining process will be retrieved from this function
getRuntime()
Total amount of runtime taken by the mining process will be retrieved from this function
convert(value)
To convert the given user specified value
FSFIMining( prefix, prefixLen, fsFim, minSup)
Method generate FFI from prefix
construct(px, py)
A function to construct Fuzzy itemSet from 2 fuzzy itemSets
Intersection(neighbourX,neighbourY)
Return common neighbours of 2 itemSet Neighbours
findElementWithTID(uList, tid)
To find element with same tid as given
WriteOut(prefix, prefixLen, item, sumIUtil,period)
To Store the patten
**Executing the code on terminal :**
----------------------------------------
.. code-block:: console
Format:
(.venv) $ python3 FGPFPMiner_old.py <inputFile> <outputFile> <neighbours> <minSup> <maxPer> <sep>
Example Usage:
(.venv) $ python3 FGPFPMiner_old.py sampleTDB.txt output.txt sampleN.txt 3 4
.. note:: minSup will be considered in percentage of database transactions
**Sample run of importing the code:**
------------------------------------------
from PAMI.fuzzyGeoreferencedPeriodicFrequentPattern import FGPFPMiner as alg
obj = alg.FFSPMiner("input.txt", "neighbours.txt", 3, 4)
obj.mine()
print("Total number of fuzzy frequent spatial patterns:", len(obj.getPatterns()))
obj.save("outputFile")
print("Total Memory in USS:", obj.getMemoryUSS())
print("Total Memory in RSS", obj.getMemoryRSS())
print("Total ExecutionTime in seconds:", obj.getRuntime())
**Credits:**
----------------
The complete program was written by B.Sai Chitra and Kundai Kwangwari under the supervision of Professor Rage Uday Kiran.
"""
_startTime = float()
_endTime = float()
_minSup = str()
_maxPer = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_nFile = " "
_FuzFile = " "
_memoryUSS = float()
_memoryRSS = float()
_sep = "\t"
_transactionsDB = []
_fuzzyValuesDB = []
def __init__(self, iFile, nFile, FuzFile, minSup, maxPer):
super().__init__(iFile, nFile, FuzFile, minSup, maxPer)
self.oFile = None
self._mapItemNeighbours = {}
self._startTime = 0
self._endTime = 0
self._itemsCnt = 0
self._itemSupData = {}
self._mapItemSum = {}
self._finalClosedPeriodicPatterns = {}
self._mapItemRegions = {}
self._fuzzyRegionReferenceMap = {}
self._joinsCnt = 0
self._BufferSize = 200
self._itemSetBuffer = []
self._finalPatterns = {}
self._finalPeriodicPatterns = {}
self._tidList = {}
self._dbLen = 0
self._regionsNumber = 0
self._RegionsCal = []
self._RegionsLabel = []
self._LabelKey = {}
def _compareItems(self, o1, o2):
"""
A Function that sort all FFI-list in ascending order of Support
:param o1: First FFI-list
:type o1: _FFList
:param o2: Second FFI-list
:type o2: _FFList
:return: Comparison Value
:rtype: int
"""
compare = self._mapItemSum[o1.item] - self._mapItemSum[o2.item]
if compare == 0:
return int(o1.item) - int(o2.item)
else:
return compare
def _convert(self, value) -> float:
"""
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 = (self._dbLen * value)
if type(value) is str:
if '.' in value:
value = float(value)
else:
value = int(value)
return value
def _fuzzyMembershipFunc(self):
try:
with open(self._FuzFile, 'r', encoding='utf-8') as f:
count = 0
for line in f:
line = line.split("\n")[0]
parts = line.split(" ")
lowerBound = parts[0].strip()
upperBound = parts[1].strip()
lb_Label = parts[2].strip()
ub_Label = parts[3].strip()
self._RegionsCal.append([int(lowerBound), int(upperBound)])
self._RegionsLabel.append([lb_Label, ub_Label])
for i in range(0, 2):
if lb_Label.capitalize() not in self._LabelKey:
self._LabelKey[lb_Label.capitalize()] = count
count += 1
if ub_Label.capitalize() not in self._LabelKey:
self._LabelKey[ub_Label.capitalize()] = count
count += 1
except IOError:
print("File Not Found")
quit()
def _creatingItemSets(self):
"""
Storing the complete transactions of the database/input file in a database variable
"""
self._transactionsDB, self._fuzzyValuesDB = [], []
if isinstance(self._iFile, _ab._pd.DataFrame):
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'Transactions' in i:
self._transactionsDB = self._iFile['Transactions'].tolist()
if 'fuzzyValues' in i:
self._fuzzyValuesDB = self._iFile['fuzzyValues'].tolist()
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.split("\n")[0]
parts = line.split(":")
items = parts[0].split(self._sep)
quantities = parts[2].split(self._sep)
self._transactionsDB.append([x for x in items])
self._fuzzyValuesDB.append([x for x in quantities])
else:
try:
with open(self._iFile, 'r', encoding='utf-8') as f:
for line in f:
line = line.split("\n")[0]
parts = line.split(":")
parts[0] = parts[0].strip()
parts[2] = parts[2].strip()
items = parts[0].split(self._sep)
quantities = parts[2].split(self._sep)
self._transactionsDB.append([x for x in items])
self._fuzzyValuesDB.append([x for x in quantities])
except IOError:
print("File Not Found")
quit()
def _mapNeighbours(self):
"""
A function to map items to their Neighbours
"""
self._mapItemNeighbours = {}
if isinstance(self._nFile, _ab._pd.DataFrame):
data, items = [], []
if self._nFile.empty:
print("its empty..")
i = self._nFile.columns.values.tolist()
if 'items' in i:
items = self._nFile['items'].tolist()
if 'Neighbours' in i:
data = self._nFile['Neighbours'].tolist()
for k in range(len(items)):
self._mapItemNeighbours[items[k]] = data[k]
if isinstance(self._nFile, str):
if _ab._validators.url(self._nFile):
data = _ab._urlopen(self._nFile)
for line in data:
line = line.decode("utf-8")
line = line.split("\n")[0]
parts = [i.rstrip() for i in line.split(self._sep)]
parts = [x for x in parts]
item = parts[0]
neigh1 = []
for i in range(1, len(parts)):
neigh1.append(parts[i])
self._mapItemNeighbours[item] = neigh1
else:
try:
with open(self._nFile, 'r', encoding='utf-8') as f:
for line in f:
line = line.split("\n")[0]
parts = [i.rstrip() for i in line.split(self._sep)]
parts = [x for x in parts]
item = parts[0]
neigh1 = []
for i in range(1, len(parts)):
neigh1.append(parts[i])
self._mapItemNeighbours[item] = neigh1
except IOError:
print(self._nFile)
print("File Not Found")
quit()
def _Regions(self, quantity):
"""
param quantity:
type quantity:
"""
self._list = [0] * len(self._LabelKey)
if self._RegionsCal[0][0] < quantity <= self._RegionsCal[0][1]:
self._list[0] = 1
return
elif quantity >= self._RegionsCal[-1][0]:
self._list[-1] = 1
return
else:
for i in range(1, len(self._RegionsCal) - 1):
if self._RegionsCal[i][0] < quantity <= self._RegionsCal[i][1]:
base = self._RegionsCal[i][1] - self._RegionsCal[i][0]
for pos in range(0, 2):
if self._RegionsLabel[i][pos].islower():
self._list[self._LabelKey[self._RegionsLabel[i][pos].capitalize()]] = float(
(self._RegionsCal[i][1] - quantity) / base)
else:
self._list[self._LabelKey[self._RegionsLabel[i][pos].capitalize()]] = float(
(quantity - self._RegionsCal[i][0]) / base)
return
[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
"""
self._startTime = _ab._time.time()
self._mapNeighbours()
self._creatingItemSets()
self._fuzzyMembershipFunc()
self._finalPatterns = {}
recent_occur = {}
for line in range(len(self._transactionsDB)):
item_list = self._transactionsDB[line]
fuzzyValues_list = self._fuzzyValuesDB[line]
self._dbLen += 1
"""
This section below is for:
1.Finding the support of each item's region in the entire database
2.Finding the periodic patterns of the data
3.Trimming off the patterns whose support is less than minSupport
"""
for i in range(0, len(item_list)):
item = item_list[i]
if item in self._tidList:
self._tidList[item].append(self._dbLen - recent_occur[item][-1])
recent_occur[item].append(self._dbLen)
else:
self._tidList[item] = [self._dbLen]
recent_occur[item] = [self._dbLen]
fuzzy_ref = fuzzyValues_list[i]
if item in self._mapItemNeighbours:
if fuzzy_ref not in self._fuzzyRegionReferenceMap:
self._Regions(int(fuzzy_ref))
self._fuzzyRegionReferenceMap[fuzzy_ref] = self._list
if item in self._itemSupData.keys():
self._itemSupData[item] = [sum(i) for i in zip(self._itemSupData[item],
self._fuzzyRegionReferenceMap[fuzzy_ref])]
else:
self._itemSupData[item] = self._fuzzyRegionReferenceMap[fuzzy_ref]
for item in self._tidList.keys():
self._tidList[item].append(len(self._transactionsDB) - recent_occur[item][-1])
del recent_occur
"""
Using Maximum Scalar Cardinality Value strategy to narrow down search space and generate candidate fuzzy periodic-frequent items.
Step1. Identify the regional representative (region with max support). This is the representative that will be tested to see if its greater than given minSup
Step2. prune out all items whose regional support is less than the given minSup
Step3. At the end, sort the list of stored Candidate Frequent-Periodic Patterns in ascending order
"""
listOfFFList = []
mapItemsToFFLIST = {}
region_label = []
for i in range(0, len(self._RegionsLabel)):
if self._RegionsLabel[i][1] not in region_label:
region_label.append(str(self._RegionsLabel[i][1]))
self._minSup = self._convert(self._minSup)
for item in self._itemSupData.keys():
if max(self._itemSupData[item]) >= self._minSup:
self._mapItemSum[item] = max(self._itemSupData[item])
self._mapItemRegions[item] = region_label[self._itemSupData[item].index(self._mapItemSum[item])]
fuList = _FFList(item)
if int(self._maxPer) >= max(self._tidList[item]):
fuList.isPeriodic = True
mapItemsToFFLIST[item] = fuList
listOfFFList.append(fuList)
del self._itemSupData
del self._tidList
listOfFFList.sort(key=_ab._functools.cmp_to_key(self._compareItems))
tid = 0
for j in range(len(self._transactionsDB)):
item_list = list(set(self._transactionsDB[j]).intersection(set(self._mapItemSum.keys())))
revisedTransaction = []
for i in range(0, len(item_list)):
pair = _Pair()
pair.item = item_list[i]
fuzzy_ref = str(self._fuzzyValuesDB[j][self._transactionsDB[j].index(pair.item)])
pair.quantity = self._fuzzyRegionReferenceMap[fuzzy_ref][
region_label.index(self._mapItemRegions[pair.item])]
if pair.quantity > 0:
revisedTransaction.append(pair)
revisedTransaction.sort(key=_ab._functools.cmp_to_key(self._compareItems))
qaunt = {}
for i in range(len(revisedTransaction) - 1, -1, -1):
pair = revisedTransaction[i]
qaunt[pair.item] = pair.quantity
remainUtil = 0
temp = list(set(self._mapItemNeighbours[pair.item]).intersection(set(qaunt.keys())))
for k in temp:
remainUtil += float(qaunt[k])
del temp
remainingUtility = remainUtil
FFListObject = mapItemsToFFLIST[pair.item]
element = _Element(tid, pair.quantity, remainingUtility)
FFListObject.addElement(element)
del qaunt
tid += 1
itemNeighbours = list(self._mapItemNeighbours.keys())
self._FSFIMining(self._itemSetBuffer, 0, listOfFFList, self._minSup, itemNeighbours)
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
def _FSFIMining(self, prefix, prefixLen, FSFIM, minSup, itemNeighbours):
"""
Generates FFSPMiner from prefix
:param prefix: the prefix patterns of FFSPMiner
:type prefix: len
:param prefixLen: the length of prefix
:type prefixLen: int
:param FSFIM: the Fuzzy list of prefix itemSets
:type FSFIM: list
:param minSup: the minimum support of
:type minSup: int
:param itemNeighbours: the set of common neighbours of prefix
:type itemNeighbours: list or set
"""
for i in range(0, len(FSFIM)):
_FFListObject1 = FSFIM[i]
if _FFListObject1.sumIUtil >= minSup:
self._WriteOut(prefix, prefixLen, _FFListObject1, _FFListObject1.sumIUtil)
newNeighbourList = self._Intersection(self._mapItemNeighbours.get(_FFListObject1.item), itemNeighbours)
if _FFListObject1.sumRUtil >= minSup:
exULs = []
for j in range(i + 1, len(FSFIM)):
_FFListObject2 = FSFIM[j]
if _FFListObject2.item in newNeighbourList:
exULs.append(self._construct(_FFListObject1, _FFListObject2))
self._joinsCnt += 1
self._itemSetBuffer.insert(prefixLen, _FFListObject1.item)
self._FSFIMining(self._itemSetBuffer, prefixLen + 1, exULs, minSup, newNeighbourList)
def _Intersection(self, neighbourX, neighbourY):
"""
A function to get common neighbours from 2 itemSets
:param neighbourX: the set of neighbours of itemSet 1
:type neighbourX: set or list
:param neighbourY: the set of neighbours of itemSet 2
:type neighbourY: set or list
:return: set of common neighbours of 2 itemSets
:rtype: set
"""
result = []
if neighbourX is None or neighbourY is None:
return result
for i in range(0, len(neighbourX)):
if neighbourX[i] in neighbourY:
result.append(neighbourX[i])
return result
[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
def _construct(self, _FFListObject1, _FFListObject2):
"""
A function to construct a new Fuzzy itemSet from 2 fuzzy itemSets
:param _FFListObject1: the itemSet px
:type _FFListObject1: FFI-List
:param _FFListObject2: itemSet py
:type _FFListObject2: FFI-List
:return: the itemSet of pxy(px and py)
:rtype: FFI-List
"""
recent_occur, first_occur, tid = 0, 0, 0
periodlist = []
_newFFListObject = _FFList(_FFListObject2.item)
for Ob1Element in _FFListObject1.elements:
Ob2Element = self._findElementWithTID(_FFListObject2, Ob1Element.tid)
if Ob2Element is None:
continue
tid = Ob1Element.tid
if len(periodlist) == 0:
periodlist.append(abs(first_occur - tid))
recent_occur = tid
else:
periodlist.append(tid - recent_occur)
recent_occur = tid
newElement = _Element(Ob1Element.tid, min([Ob1Element.iUtils, Ob2Element.iUtils], key=lambda x: float(x)),
Ob2Element.rUtils)
_newFFListObject.addElement(newElement)
if periodlist and int(self._maxPer) >= max(periodlist):
_newFFListObject.isPeriodic = True
else:
_newFFListObject.isPeriodic = False
return _newFFListObject
def _findElementWithTID(self, uList, tid):
"""
To find element with same tid as given
:param uList: fuzzyList
:type uList: FFI-List
:param tid: transaction id
:type tid: int
:return: element tid as given
:rtype: element if exist or None
"""
List = uList.elements
first = 0
last = len(List) - 1
while first <= last:
mid = (first + last) >> 1
if List[mid].tid < tid:
first = mid + 1
elif List[mid].tid > tid:
last = mid - 1
else:
return List[mid]
return None
def _WriteOut(self, prefix, prefixLen, _FFListObject, sumIUtil):
"""
To Store the patten
:param prefix: prefix of itemSet
:type prefix: list
:param prefixLen: length of prefix
:type prefixLen: int
:param _FFListObject: the last item
:type _FFListObject: int
:param sumIUtil: sum of utility of itemSet
:type sumIUtil: float
"""
item = _FFListObject.item
self._itemsCnt += 1
res = ""
for i in range(0, prefixLen):
res += str(prefix[i]) + "." + str(self._mapItemRegions[prefix[i]]) + "\t"
res += str(item) + "." + str(self._mapItemRegions.get(item))
res1 = str(sumIUtil)
self._finalPatterns[res] = res1
if _FFListObject.isPeriodic:
self._finalPeriodicPatterns[res] = res1
[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._finalPeriodicPatterns.items():
data.append([a.replace('\t', ' '), b])
dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
return dataFrame
[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._finalPeriodicPatterns
[docs]
def save(self, outFile):
"""
Complete set of frequent patterns will be loaded in to an output file
:param outFile: name of the output file
:type outFile: csv file
"""
self.oFile = outFile
keylist = (self._finalPatterns.keys())
writer = open(self.oFile, 'w+')
for x in keylist:
patternsAndSupport = x.strip() + ":" + str(self._finalPatterns[x])
writer.write("%s \n" % patternsAndSupport)
[docs]
def printResults(self):
"""
This function is used to print the results
"""
print("Total number of Spatial Fuzzy 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())
[docs]
def getPatternsAsDataframe(self):
"""
:return: returning periodic frequent patterns in a dataframe
:rtype: pd.DataFrame
"""
data = []
dataFrame = _ab._pd.DataFrame()
for a, b in self._finalPeriodicPatterns.items():
data.append([a, b])
dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
return dataFrame
[docs]
def generateLatexCode(self, result):
titles = result.columns.tolist()
titles.remove("minsup")
titles.remove("algorithm")
for i in range(0, len(titles)):
legendary = pd.unique(result[['algorithm']].values.ravel())
color = ['red', 'blue', 'green', 'black', 'yellow']
xaxis = result["minsup"].values.tolist()
yaxis = result[titles[i]].values.tolist()
algo = result["algorithm"].values.tolist()
x_label = "minsup"
filename = titles[i]
latexwriter = open(filename + "Latexfile.tex", "w")
latexwriter.write("")
latexwriter.write("\\begin{axis}[\n\txlabel={\\Huge{" + x_label + "}},")
latexwriter.write("\n\tylabel={\\Huge{" + titles[i] + "}},")
latexwriter.write("\n\txmin=" + str(min(xaxis)) + ", xmax=" + str(max(xaxis)) + ",")
for num in range(0, len(legendary)):
latexwriter.write("\n\\addplot+ [" + color[num] + "]\n\tcoordinates {\n")
for num2 in range(0, len(xaxis)):
if legendary[num] == algo[num2]:
latexwriter.write("(" + str(xaxis[num2]) + "," + str(yaxis[num2]) + ")\n")
latexwriter.write("\t}; \\addlegendentry{" + legendary[num] + "}\n")
if num + 1 == len(legendary):
latexwriter.write("\\end{axis}")
print("Latex file generated successfully")
[docs]
def generateGraphs(self, result):
fig = px.line(result, x='minsup', y='patterns', color='algorithm', title='Patterns)', markers=True)
fig.show()
fig = px.line(result, x='minsup', y='runtime', color='algorithm', title='Runtime)', markers=True)
fig.show()
fig = px.line(result, x='minsup', y='memoryUSS', color='algorithm', title='MemoryUSS)', markers=True)
fig.show()
fig = px.line(result, x='minsup', y='memoryRSS', color='algorithm', title='MemoryRSS)', markers=True)
fig.show()
if __name__ == "__main__":
_ap = str()
if len(_ab._sys.argv) == 6 or len(_ab._sys.argv) == 7:
if len(_ab._sys.argv) == 7:
_ap = FGPFPMiner(_ab._sys.argv[1], _ab._sys.argv[2], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5],
_ab._sys.argv[6])
if len(_ab._sys.argv) == 6:
_ap = FGPFPMiner(_ab._sys.argv[1], _ab._sys.argv[2], _ab._sys.argv[3], _ab._sys.argv[4], _ab._sys.argv[5],_ab._sys.argv[6])
_ap.mine()
_ap.mine()
print("Total number of Spatial Fuzzy 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())
_ap.save("outputfile.txt")
else:
print("Error! The number of input parameters do not match the total number of parameters provided")