UVECLAT

class PAMI.uncertainFrequentPattern.basic.UVECLAT.UVEclat(iFile, minSup, sep='\t')[source]

Bases: _frequentPatterns

About this algorithm

Description:

It is one of the fundamental algorithm to discover frequent patterns in an uncertain transactional database using PUF-Tree.

Reference:

Carson Kai-Sang Leung, Lijing Sun: “Equivalence class transformation based mining of frequent itemsets from uncertain data”, SAC ‘11: Proceedings of the 2011 ACM Symposium on Applied ComputingMarch, 2011, Pages 983–984, https://doi.org/10.1145/1982185.1982399

Attributes:

iFilefile: Name of the Input file or path of the input file
oFilefile: Name of the output file or path of the output file
minSupfloat 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
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.
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
startTime:float: To record the start time of the mining process
endTime:float: To record the completion time of the mining process
Databaselist: To store the transactions of a database in list
mapSupportDictionary: To maintain the information of item and their frequency
lnoint: To represent the total no of transaction
treeclass: To represent the Tree class
itemSetCountint: To represents the total no of patterns
finalPatternsdict: To store the complete patterns

Methods:

mine(): Mining process will start from here
getPatterns(): Complete set of patterns will be retrieved with this function
storePatternsInFile(oFile): Complete set of frequent patterns will be loaded in to a output file
getPatternsInDataFrame(): 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(fileName): Scans the dataset and stores in a list format
frequentOneItem(): Extracts the one-length frequent patterns from database

Execution methods

Terminal command

Format:

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

Example Usage:

(.venv) $ python3 uveclat.py sampleDB.txt patterns.txt 3

Note

minSup can be specified in support count or a value between 0 and 1.

Calling from a python program

from PAMI.uncertainFrequentPattern.basic import UVECLAT as alg

iFile = 'sampleDB.txt'

minSup = 10  # can also be specified between 0 and 1

obj = alg.UVEclat(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

print("Total number of Frequent Patterns:", len(frequentPatterns))

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.

getMemoryRSS()[source]

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()[source]

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()[source]

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

Returns:: returning frequent patterns
Return type:: dict

getPatternsAsDataFrame()[source]

Storing final frequent patterns in a dataframe

Returns:: returning frequent patterns in a dataframe
Return type:: pd.DataFrame

getRuntime()[source]

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

mine()[source]: Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

printResults()[source]: This function is used to print the results

save(oFile)[source]

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

Parameters:: oFile (csv file) – name of the output file

startMine() → None[source]: Code for the mining process will start from this function