PAMI.partialPeriodicPattern.closed package

Submodules

PAMI.partialPeriodicPattern.closed.PPPClose module

class PAMI.partialPeriodicPattern.closed.PPPClose.PPPClose(iFile, periodicSupport, period, sep='\t')[source]

Bases: _partialPeriodicPatterns

Description:

PPPClose algorithm is used to discover the closed partial periodic patterns in temporal databases. It uses depth-first search.

Reference:

R. Uday Kiran1 , J. N. Venkatesh2 , Philippe Fournier-Viger3 , Masashi Toyoda1 , P. Krishna Reddy2 and Masaru Kitsuregawa https://www.tkl.iis.u-tokyo.ac.jp/new/uploads/publication_file/file/799/PAKDD.pdf

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.

  • iFile – str : Name of the Input file to mine complete set of frequent pattern’s

  • oFile – str : Name of the output file to store complete set of frequent patterns

  • period – float: Minimum partial periodic…

  • periodicSupport – float: Minimum partial periodic…

  • 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

oFilestr

Name of the output file or path of the input file

periodicSupport: int or float or str

The user can specify periodicSupport either in count or proportion of database size. If the program detects the data type of periodicSupport is integer, then it treats periodicSupport is expressed in count. Otherwise, it will be treated as float. Example: periodicSupport=10 will be treated as integer, while periodicSupport=10.0 will be treated as float

period: int or float or str

The user can specify period either in count or proportion of database size. If the program detects the data type of period is integer, then it treats period is expressed in count. Otherwise, it will be treated as float. Example: period=10 will be treated as integer, while period=10.0 will be treated as float

sepstr

This variable is used to distinguish items from one another in a transaction. The default separator is tab space or . However, the users can override their default separator.

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

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 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

Executing the code on terminal:

Format:

(.venv) $ python3 PPPClose.py <inputFile> <outputFile> <periodicSupport> <period>

Examples:

(.venv) $ python3 PPPClose.py sampleTDB.txt patterns.txt 0.3 0.4

Sample run of the imported code:

from PAMI.partialPeriodicPattern.closed import PPPClose as alg

obj = alg.PPPClose("../basic/sampleTDB.txt", "2", "6")

obj.startMine()

periodicFrequentPatterns = obj.getPatterns()

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

obj.save("patterns")

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.

Mine()[source]

Mining process will start from here

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

printResults()[source]

To print all the results of execution

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]

Mining process will start from here

PAMI.partialPeriodicPattern.closed.abstract module

Module contents