PAMI.partialPeriodicFrequentPattern.basic package

Submodules

PAMI.partialPeriodicFrequentPattern.basic.GPFgrowth module

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

PAMI.partialPeriodicFrequentPattern.basic.PPF_DFS module

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

Bases: partialPeriodicPatterns, ABC

About this algorithm

Description:

PPF_DFS is algorithm to mine the partial periodic frequent patterns.

References:

(Has to be added)

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.

  • Generation(prefix, itemsets, tidsets) – Used to implement prefix class equibalence method to generate the periodic patterns recursively.

  • getPartialPeriodicPatterns() – 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 PPF_DFS.py <inputFile> <outputFile> <minSup> <maxPer> <minPR>

Example Usage:

(.venv) $ python3 PPF_DFS.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 PPF_DFS 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.PPF_DFS(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]

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 program start with extracting the periodic frequent items from the database and performs prefix equivalence to form the combinations and generates closed periodic frequent patterns.

oFile = None
printResults()[source]

this function is used to print the results

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

PAMI.partialPeriodicFrequentPattern.basic.abstract module

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

Bases: ABC

Description:

This abstract base class defines the variables and methods that every partial periodic pattern mining algorithm must employ in PAMI

Attributes:
iFilestr

Input file name or path of the input file

minSup: float

UserSpecified minimum support value. It has to be given in terms of count of total number of transactions in the input database/file

startTime:float

To record the start time of the algorithm

endTime:float

To record the completion time of the algorithm

finalPatterns: dict

Storing the complete set of patterns in a dictionary variable

oFilestr

Name of the output file to store complete set of frequent patterns

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

getFrequentPatterns()

Complete set of patterns will be retrieved with this function

save(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 data frame

getMemoryUSS()

Total amount of USS memory consumed by the program will be retrieved from this function

getMemoryRSS()

Total amount of RSS memory consumed by the program will be retrieved from this function

getRuntime()

Total amount of runtime taken by the program will be retrieved from this function

abstract getMemoryRSS()[source]

Total amount of RSS memory consumed by the program will be retrieved from this function

abstract getMemoryUSS()[source]

Total amount of USS memory consumed by the program will be retrieved from this function

abstract getPatterns()[source]

Complete set of frequent patterns generated will be retrieved from this function

abstract getPatternsAsDataFrame()[source]

Complete set of frequent patterns will be loaded in to data frame from this function

abstract getRuntime()[source]

Total amount of runtime taken by the program will be retrieved from this function

abstract printResults()[source]

To print all the results of execution.

abstract save(oFile)[source]

Complete set of frequent patterns will be saved in to an output file from this function :param oFile: Name of the output file :type oFile: csv file

abstract startMine()[source]

Code for the mining process will start from this function

Module contents