PAMI.uncertainFaultTolerantFrequentPattern package

Submodules

PAMI.uncertainFaultTolerantFrequentPattern.VBFTMine module

class PAMI.uncertainFaultTolerantFrequentPattern.VBFTMine.VBFTMine(iFile, minSup, itemSup, minLength, faultTolerance, sep='\t')

Bases: _faultTolerantFrequentPatterns

Description:

VBFTMine is one of the fundamental algorithm to discover fault tolerant frequent patterns in an uncertain transactional database based on bitset representation. This program employs apriori property (or downward closure property) to reduce the search space effectively.

Reference:

Koh, JL., Yo, PW. (2005). An Efficient Approach for Mining Fault-Tolerant Frequent Patterns Based on Bit Vector Representations. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_51

Parameters:

• iFile – str : Name of the Input file to mine complete set of uncertain Fault Tolerant FrequentFrequent Patterns

• oFile – str : Name of the output file to store complete set of uncertain Fault Tolerant FrequentFrequent Patterns

• 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

• itemSup – int or float : Frequency of an item

• minLength – int minimum length of a pattern

• faultTolerance – int : The ability of a pattern mining algorithm to handle errors or inconsistencies in the data without completely failing or producing incorrect results.

• 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:

startTimefloat

To record the start time of the mining process

endTimefloat

To record the completion time of the mining process

finalPatternsdict

Storing the complete set of patterns in a dictionary variable

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

Databaselist

To store the transactions of a database in list

Executing the code on terminal:

Format:

(.venv) $ python3 VBFTMine.py <inputFile> <outputFile> <minSup> <itemSup> <minLength> <faultTolerance>

Examples usage:

(.venv) $ python3 VBFTMine.py sampleDB.txt patterns.txt 10.0 3.0 3 1


        .. note:: minSup will be considered in times of minSup and count of database transactions

Sample run of the importing code:

import PAMI.faultTolerantFrequentPattern.basic.VBFTMine as alg

obj = alg.VBFTMine(iFile, minSup, itemSup, minLength, faultTolerance)

obj.startMine()

faultTolerantFrequentPattern = obj.getPatterns()

print("Total number of Fault Tolerant Frequent Patterns:", len(faultTolerantFrequentPattern))

obj.save(oFile)

Df = obj.getPatternInDataFrame()

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 P.Likhitha under the supervision of Professor Rage Uday Kiran.

Mine()

Frequent pattern mining process will start from here

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 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 (file) – name of the output file

startMine()

Frequent pattern mining process will start from here

PAMI.uncertainFaultTolerantFrequentPattern.abstract module

Module contents