GPFgrowth

class PAMI.partialPeriodicFrequentPattern.basic.GPFgrowth.GPFgrowth(iFile, minSup, maxPer, minPR, sep='\t')[source]

Bases: partialPeriodicPatterns, ABC

About this algorithm

Description:

GPFgrowth is algorithm to mine the partial periodic frequent pattern in temporal database.

Reference:

R. Uday Kiran, J.N. Venkatesh, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy, Discovering partial periodic-frequent patterns in a transactional database, Journal of Systems and Software, Volume 125, 2017, Pages 170-182, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2016.11.035.

Parameters:

  • iFile (str) – Name of the Input file to mine complete set of correlated patterns.

  • oFile (str) – Name of the output file to store complete set of correlated patterns.

  • 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.

  • minPR (str) – Controls the maximum number of transactions in which any two items within a pattern can reappear.

  • maxPer (str) – Controls the maximum number of transactions in which any two items within a pattern can reappear.

  • 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:

  • 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.

  • minSup (int) – The user given minSup.

  • maxPer (int) – The user given maxPer.

  • minPR (int) – The user given minPR.

  • finalPatterns (dict) – It represents to store the pattern.

Methods:

  • mine() – Mining process will start from here.

  • getPatterns() – Complete set of patterns will be retrieved with this function.

  • storePatternsInFile(ouputFile) – Complete set of frequent patterns will be loaded in to an output file.

  • getPatternsAsDataFrame() – Complete set of frequent patterns will be loaded in to an output file.

  • 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.

Execution methods

Terminal command

Format:

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

Example Usage:

(.venv) $ python3 GPFgrowth.py sampleTDB.txt output.txt 0.25 300 0.7

Note

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

Calling from a python program

from PAMI.partialPeriodicFrequentPattern.basic import GPFgrowth as alg

iFile = 'sampleTDB.txt'

minSup = 0.25 # can be specified between 0 and 1

maxPer = 300 # can  be specified between 0 and 1

minPR = 0.7 # can  be specified between 0 and 1

obj = alg.GPFgrowth(inputFile, minSup, maxPer, minPR, sep)

obj.mine()

partialPeriodicFrequentPatterns = obj.getPatterns()

print("Total number of partial periodic Patterns:", len(partialPeriodicFrequentPatterns))

obj.save(oFile)

Df = obj.getPatternInDf()

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 Nakamura and revised by Tarun Sreepada 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]

This function returns the final partial Periodic Patterns.

Returns:

dictionary

getPatternsAsDataFrame()[source]

Storing final periodic-frequent patterns in a dataframe

Returns:

returning periodic-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]
oFile = None
printResults()[source]

This function is used to print the results

runTime = 0
save(outFile)[source]

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

Parameters:

outFile (csv file) – name of the output file

startMine()[source]

Code for the mining process will start from this function