PAMI.highUtilitySpatialPattern.topk package

Submodules

PAMI.highUtilitySpatialPattern.topk.TKSHUIM module

class PAMI.highUtilitySpatialPattern.topk.TKSHUIM.Dataset(datasetpath, sep)

Bases: object

A class represent the list of transactions in this dataset

Attributes:

transactions:

the list of transactions in this dataset

maxItem:

the largest item name

Methods:

createTransaction(line):

Create a transaction object from a line from the input file

getMaxItem():

return Maximum Item

getTransactions():

return transactions in database

createTransaction(line)

A method to create Transaction from dataset given

Parameters:

line (string) – represent a single line of database

:return : Transaction. :rtype: int

getMaxItem()

A method to return name of the largest item

getTransactions()

A method to return transactions from database

maxItem = 0

transactions = []

class PAMI.highUtilitySpatialPattern.topk.TKSHUIM.TKSHUIM(iFile, nFile, k, sep='\t')

Bases: utilityPatterns

Description:

Top K Spatial High Utility ItemSet Mining (TKSHUIM) aims to discover Top-K Spatial High Utility Itemsets (TKSHUIs) in a spatioTemporal database

Reference:

P. Pallikila et al., “Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases,” 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 4925-4935, doi: 10.1109/BigData52589.2021.9671912.

Parameters:

• iFile – str : Name of the Input file to mine complete set of High Utility Spatial patterns

• oFile – str : Name of the output file to store complete set of High Utility Spatial patterns

• minUtil – int : Minimum utility threshold given by User

• maxMemory – int : Maximum memory used by this program for running

• candidateCount – int : Number of candidates to consider when calculating a high utility spatial pattern

• nFile – str : Name of the input file to mine complete set of High Utility Spatial patterns

• 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:

iFilefile

Name of the input file to mine complete set of frequent patterns

nFilefile

Name of the Neighbours file that contain neighbours of items

oFilefile

Name of the output file to store complete set of frequent patterns

memoryRSSfloat

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

kint

The user given k value

candidateCount: int

Number of candidates

utilityBinArrayLU: list

A map to hold the pmu values of the items in database

utilityBinArraySU: list

A map to hold the subtree utility values of the items is database

oldNamesToNewNames: list

A map to hold the subtree utility values of the items is database

newNamesToOldNames: list

A map to store the old name corresponding to new name

Neighboursmap

A dictionary to store the neighbours of a item

maxMemory: float

Maximum memory used by this program for running

itemsToKeep: list

keep only the promising items ie items having twu >= minUtil

itemsToExplore: list

keep items that subtreeUtility grater than minUtil

Methods:

mine()

Mining process will start from here

getPatterns()

Complete set of patterns will be retrieved with this function

save(oFile)

Complete set of patterns will be loaded in to a output file

getPatternsAsDataFrame()

Complete set of 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

calculateNeighbourIntersection(self, prefixLength)

A method to return common Neighbours of items

backtrackingEFIM(transactionsOfP, itemsToKeep, itemsToExplore, prefixLength)

A method to mine the TKSHUIs Recursively

useUtilityBinArraysToCalculateUpperBounds(transactionsPe, j, itemsToKeep, neighbourhoodList)

A method to calculate the sub-tree utility and local utility of all items that can extend itemSet P and e

output(tempPosition, utility)

A method ave a high-utility itemSet to file or memory depending on what the user chose

is_equal(transaction1, transaction2)

A method to Check if two transaction are identical

intersection(lst1, lst2)

A method that return the intersection of 2 list

useUtilityBinArrayToCalculateSubtreeUtilityFirstTime(dataset)

Scan the initial database to calculate the subtree utility of each items using a utility-bin array

sortDatabase(self, transactions)

A Method to sort transaction in the order of PMU

sort_transaction(self, trans1, trans2)

A Method to sort transaction in the order of PMU

useUtilityBinArrayToCalculateLocalUtilityFirstTime(self, dataset)

A method to scan the database using utility bin array to calculate the pmus

Executing the code on terminal:

Format:

(.venv) $ python3 TKSHUIM.py <inputFile> <outputFile> <Neighbours> <k> <sep>

Example Usage:

(.venv) $ python3 TKSHUIM.py sampleTDB.txt output.txt sampleN.txt 35

Note

maxMemory will be considered as Maximum memory used by this program for running

Sample run of importing the code:

from PAMI.highUtilitySpatialPattern.topk import TKSHUIM as alg

obj=alg.TKSHUIM("input.txt","Neighbours.txt",35)

obj.mine()

Patterns = obj.getPatterns()

obj.save("output")

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 Pradeep Pallikila under the supervision of Professor Rage Uday Kiran.

Neighbours = {}

additemset(itemset, utility)

adds the itemset to the priority queue

Parameters:

• itemset (str) – the itemset to be added

• utility (numpy.array) – utility matrix for the itemset to be added

backtrackingEFIM(transactionsOfP, itemsToKeep, itemsToExplore, prefixLength)

A method to mine the TKSHUIs Recursively

Parameters:

• transactionsOfP (list) – the list of transactions containing the current prefix P

• itemsToKeep (list) – the list of secondary items in the p-projected database

• itemsToExplore (list) – the list of primary items in the p-projected database

• prefixLength (int) – current prefixLength

calculateNeighbourIntersection(prefixLength)

A method to find common Neighbours

Parameters:

prefixLength – the prefix itemSet

:type prefixLength:int

candidateCount = 0

endTime = 0.0

finalPatterns = {}

getMemoryRSS()

Total amount of RSS memory consumed by the mining process will be retrieved from this function

Returns:

returning RSS memory consumed by the mining process

Return type:

float

getMemoryUSS()

Total amount of USS memory consumed by the mining process will be retrieved from this function

Returns:

returning USS memory consumed by the mining process

Return type:

float

getPatterns()

Function to send the set of patterns after completion of the mining process

Returns:

returning patterns

Return type:

dict

getPatternsAsDataFrame()

Storing final patterns in a dataframe

Returns:

returning patterns in a dataframe

Return type:

pd.DataFrame

getRuntime()

Calculating the total amount of runtime taken by the mining process

Returns:

returning total amount of runtime taken by the mining process

Return type:

float

heapList = []

iFile = ' '

intTostr = {}

intersection(lst1, lst2)

A method that return the intersection of 2 list

Parameters:

• lst1 (list) – items neighbour to item1

• lst2 (list) – items neighbour to item2

:return :intersection of two lists :rtype : list

is_equal(transaction1, transaction2)

A method to Check if two transaction are identical

Parameters:

• transaction1 (Transaction) – the first transaction.

• transaction2 (Transaction) – the second transaction.

:return : whether both are identical or not :rtype: bool

maxMemory = 0

memoryRSS = 0.0

memoryUSS = 0.0

minUtil = 0

mine()

Main function of the program.

nFile = ' '

newNamesToOldNames = {}

oFile = ' '

oldNamesToNewNames = {}

output(tempPosition, utility)

A method save all high-utility itemSet to file or memory depending on what the user chose

Parameters:

tempPosition – position of last item

:type tempPosition : int :param utility: total utility of itemSet :type utility: int

printResults()

This function is used to print the results

save(outFile)

Complete set of patterns will be loaded in to an output file

Parameters:

outFile (csv file) – name of the output file

sep = '\t'

sortDatabase(transactions)

A Method to sort transaction in the order of PMU

Parameters:

transactions (Transaction) – transaction of items

Returns:

sorted transaction

Return type:

Transaction

sort_transaction(trans1, trans2)

A Method to sort transaction in the order of PMU

Parameters:

trans1 (Transaction) – the first transaction.

:param trans2:the second transaction. :type trans2: Transaction :return: sorted transaction. :rtype: int

startMine()

Main function of the program.

startTime = 0.0

strToint = {}

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useUtilityBinArrayToCalculateLocalUtilityFirstTime(dataset)

A method to scan the database using utility bin array to calculate the pmus

Parameters:

dataset (database) – the transaction database.

useUtilityBinArrayToCalculateSubtreeUtilityFirstTime(dataset)

Scan the initial database to calculate the subtree utility of each item using a utility-bin array

Parameters:

dataset (Dataset) – the transaction database

useUtilityBinArraysToCalculateUpperBounds(transactionsPe, j, itemsToKeep, neighbourhoodList)

A method to calculate the sub-tree utility and local utility of all items that can extend itemSet P U {e}

Parameters:

transactionsPe (list) – transactions the projected database for P U {e}

:param j:the position of j in the list of promising items :type j:int :param itemsToKeep :the list of promising items :type itemsToKeep: list :param neighbourhoodList: list of neighbourhood elements :type neighbourhoodList: list

utilityBinArrayLU = {}

utilityBinArraySU = {}

class PAMI.highUtilitySpatialPattern.topk.TKSHUIM.Transaction(items, utilities, transactionUtility, pmus=None)

Bases: object

A class to store Transaction of a database

Attributes:

items: list

A list of items in transaction

utilities: list

A list of utilites of items in transaction

transactionUtility: int

represent total sum of all utilities in the database

pmus: list

represent the pmu (probable maximum utility) of each element in the transaction

prefixutility:

prefix Utility values of item

offset:

an offset pointer, used by projected transactions

Methods:

projectedTransaction(offsetE):

A method to create new Transaction from existing till offsetE

getItems():

return items in transaction

getUtilities():

return utilities in transaction

getPmus():

return pmus in transaction

getLastPosition():

return last position in a transaction

removeUnpromisingItems():

A method to remove items with low Utility than minUtil

insertionSort():

A method to sort all items in the transaction

getItems()

A method to return items in transaction

getLastPosition()

A method to return last position in a transaction

getPmus()

A method to return pmus in transaction

getUtilities()

A method to return utilities in transaction

insertionSort()

A method to sort items in order

offset = 0

prefixUtility = 0

projectTransaction(offsetE)

A method to create new Transaction from existing till offsetE

Parameters:

offsetE (int) – an offset over the original transaction for projecting the transaction

removeUnpromisingItems(oldNamesToNewNames)

A method to remove items with low Utility than minUtil

Parameters:

oldNamesToNewNames (map) – A map represent old namses to new names

PAMI.highUtilitySpatialPattern.topk.TKSHUIM.main()

PAMI.highUtilitySpatialPattern.topk.abstract module

class PAMI.highUtilitySpatialPattern.topk.abstract.utilityPatterns(iFile, nFile, k, sep='\t')

Bases: ABC

Description:

This abstract base class defines the variables and methods that every topk spatial high utility pattern mining algorithm must employ in PAMI

Attributes:

iFilestr

Input file name or path of the input file

k: integer

The user can specify k (top-k)

sepstr

This variable is used to distinguish items from one another in a transaction. The default seperator is tab space or . However, the users can override their default separator

startTime:float

To record the start time of the algorithm

endTime:float

To record the completion time of the algorithm

finalPatterns: dict

Storing the complete set of patterns in a dictionary variable

oFilestr

Name of the output file to store complete set of frequent patterns

memoryUSSfloat

To store the total amount of USS memory consumed by the program

memoryRSSfloat

To store the total amount of RSS memory consumed by the program

Methods:

mine()

Calling this function will start the actual mining process

getPatterns()

This function will output all interesting patterns discovered by an algorithm

save(oFile)

This function will store the discovered patterns in an output file specified by the user

getPatternsAsDataFrame()

The function outputs the patterns generated by an algorithm as a data frame

getMemoryUSS()

This function outputs the total amount of USS memory consumed by a mining algorithm

getMemoryRSS()

This function outputs the total amount of RSS memory consumed by a mining algorithm

getRuntime()

This function outputs the total runtime of a mining algorithm

abstract endTime()

Variable to store the end time of the complete program

abstract finalPatterns()

Variable to store the complete set of patterns in a dictionary

abstract getMemoryRSS()

Total amount of RSS memory consumed by the program will be retrieved from this function

abstract getMemoryUSS()

Total amount of USS memory consumed by the program will be retrieved from this function

abstract getPatterns()

Complete set of patterns generated will be retrieved from this function

abstract getPatternsAsDataFrame()

Complete set of generated patterns will be loaded in to data frame from this function

abstract getRuntime()

Total amount of runtime taken by the program will be retrieved from this function

abstract iFile()

Variable to store the input file path/file name

abstract memoryRSS()

Variable to store RSS memory consumed by the program

abstract memoryUSS()

Variable to store USS memory consumed by the program

abstract nFile()

Variable to store the neighbourhood file path/file name

abstract oFile()

Variable to store the name of the output file to store the complete set of frequent patterns

abstract printResults()

To print all the results of execution

abstract save(oFile)

Complete set of patterns will be saved in to an output file from this function

Parameters:

oFile (csv file) – Name of the output file

abstract startMine()

Code for the mining process will start from this function

abstract startTime()

Variable to store the start time of the mining process

Module contents