UPFPGrowthPlus

class PAMI.uncertainPeriodicFrequentPattern.basic.UPFPGrowthPlus.UPFPGrowthPlus(iFile, minSup, maxPer, sep='\t')

Bases: _periodicFrequentPatterns

Description:

Basic Plus is to discover periodic-frequent patterns in a uncertain temporal database.

Reference:

Palla Likhitha, Rage Veena,Rage Uday Kiran, Koji Zettsu, Masashi Toyoda, Philippe Fournier-Viger, (2023). UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_16

Parameters:

• iFile – str : Name of the Input file to mine complete set of Uncertain Periodic Frequent Patterns

• oFile – str : Name of the output file to store complete set of Uncertain Periodic Frequent patterns

• minSup – str: minimum support thresholds were tuned to find the appropriate ranges in the limited memory

• 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.

• maxper – floot : where maxPer represents the maximum periodicity threshold value specified by the user.

Attributes:

iFile: file

Name of the Input file or path of input file

oFile: file

Name of the output file or path of 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 seperator is tab space or . 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

To represent the total no of transaction

tree: class

To represents the Tree class

itemSetCount: int

To represents the total no of patterns

finalPatterns: dict

To store the complete patterns

Methods:

mine()

Mining process will start from here

getPatterns()

Complete set of patterns will be retrieved with this function

savePatterns(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 and stores in a list format

updateDatabases()

Update the database by removing aperiodic items and sort the Database by item decreased support

buildTree()

After updating the Database, remaining items will be added into the tree by setting root node as null

convert()

to convert the user specified value

PeriodicFrequentOneItems()

To extract the one-length periodic-frequent items

Executing the code on terminal:

Format:

(.venv) $ python3 UPFPGrowthPlus.py <inputFile> <outputFile> <minSup> <maxPer>

Examples Usage:

(.venv) $ python3 UPFPGrowthPlus.py sampleTDB.txt patterns.txt 0.3 4


        .. note:: minSup and maxPer will be considered in support count or frequency

Importing this algorithm into a python program

from PAMI.uncertainPeriodicFrequentPattern import UPFPGrowthPlus as alg

obj = alg.UPFPGrowthPlus(iFile, minSup, maxPer)

obj.startMine()

periodicFrequentPatterns = obj.getPatterns()

print("Total number of uncertain Periodic Frequent Patterns:", len(periodicFrequentPatterns))

obj.save(oFile)

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.

Mine()

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

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. :return: returning USS memory consumed by the mining process :rtype: float

getPatterns()

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe

Returns:

returning frequent 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

printResults()

This function is used to print the results

save(outFile)

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

Parameters:

outFile (csv file) – name of the output file

startMine()

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

PAMI.uncertainPeriodicFrequentPattern.basic.UPFPGrowthPlus.printTree(root)

To print the tree with nodes with item name, probability, timestamps, and second probability respectively.

Attributes:

Parameters:

root – Node

Returns:

print all Tree with nodes with items, probability, parent item, timestamps, second probability respectively.