kPFPMiner

class PAMI.periodicFrequentPattern.topk.kPFPMiner.kPFPMiner.kPFPMiner(iFile, k, sep='\t')[source]

Bases: _periodicFrequentPatterns

Description:

Top - K is and algorithm to discover top periodic-frequent patterns in a temporal database.

Reference:

Likhitha, P., Ravikumar, P., Kiran, R.U., Watanobe, Y. (2022).: Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databases. Big Data Analytics.

BDA 2022. Lecture Notes in Computer Science, vol 13773. Springer, Cham. https://doi.org/10.1007/978-3-031-24094-2_14

Parameters:

iFile – str : Name of the Input file to mine complete set of periodic frequent pattern’s
oFile – str : Name of the output file to store complete set of periodic frequent pattern’s
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:

iFilestr: Input file name or path of the input file
k: int: User specified counte of top-k periodic frequent patterns
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.
oFilestr: Name of the output file or the path of the output file
startTime:float: To record the start time of the mining process
endTime:float: To record the completion time of the mining process
finalPatterns: dict: 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

Methods:

mine(): Mining process will start from here
getPatterns(): Complete set of patterns will be retrieved with this function
savePatterns(oFile): Complete set of frequent patterns will be loaded in to a output file
getPatternsAsDataFrame(): 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(): Scans the dataset or dataframes and stores in list format
frequentOneItem(): Generates one frequent patterns
eclatGeneration(candidateList): It will generate the combinations of frequent items
generateFrequentPatterns(tidList): It will generate the combinations of frequent items from a list of items

Executing the code on terminal:

Format:

(.venv) $ python3 kPFPMiner.py <inputFile> <outputFile> <k>

Examples :

(.venv) $  python3 kPFPMiner.py sampleDB.txt patterns.txt 10

**Sample run of the importing code:

import PAMI.periodicFrequentPattern.kPFPMiner as alg

obj = alg.kPFPMiner(iFile, k)

obj.startMine()

periodicFrequentPatterns = obj.getPatterns()

print("Total number of top-k Periodic Frequent Patterns:", len(periodicFrequentPatterns))

obj.save(oFile)

Df = obj.getPatternInDataFrame()

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

getPer_Sup(tids)[source]

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

lno = 0

printResults()[source]

save(outFile)[source]

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

Parameters:: outFile (file) – name of the output file

startMine()[source]: Main function of the program