PFPGrowth

class PAMI.periodicFrequentPattern.basic.PFPGrowth.PFPGrowth(iFile, minSup, maxPer, sep='\t')[source]

Bases: _periodicFrequentPatterns

About this algorithm

Description:

PFPGrowth is one of the fundamental algorithm to discover periodic-frequent patterns in a transactional database.

Reference:

Syed Khairuzzaman Tanbeer, Chowdhury Farhan, Byeong-Soo Jeong, and Young-Koo Lee, “Discovering Periodic-Frequent Patterns in Transactional Databases”, PAKDD 2009, https://doi.org/10.1007/978-3-642-01307-2_24

Parameters:

  • iFile (str or URL or dataFrame) – Name of the Input file to mine complete set of frequent patterns.

  • oFile (str) – Name of the output file to store complete set of frequent 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. Otherwise, it will be treated as float.

  • maxPer (int or float or str) – The user can specify maxPer either in count or proportion of database size. It 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:

  • 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.

  • 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.

  • Database (list) – To store the transactions of a database in list.

  • mapSupport (Dictionary) – To maintain the information of item and their frequency.

  • lno (int) – It represents the total no of transactions

  • tree (class) – it represents the Tree class.

  • itemSetCount (int) – it represents the total no of patterns.

Methods:

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

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

  • save(oFile) – Complete set of periodic-frequent patterns will be loaded in to a output file.

  • getPatternsAsDataFrame() – Complete set of periodic-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(fileName) – Scans the dataset and stores in a list format.

  • PeriodicFrequentOneItem() – Extracts the one-periodic-frequent patterns from database.

  • updateDatabases() – Update the database by removing aperiodic items and sort the Database by item decreased support.

  • buildTree() – After updating the Database, remaining items will be added into the tree by setting root node as null.

  • convert() – This methos is used to convert the user specified value.

Execution methods

Terminal command

Format:

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

Example usage:

(.venv) $ python3 PFPGrowth.py sampleDB.txt patterns.txt 10.0 20.0

Note

minSup will be considered in percentage of database transactions

Calling from a python program

from PAMI.periodicFrequentPattern.basic import PFPGrowth as alg

iFile = 'sampleDB.txt'

minSup = 10  # can also be specified between 0 and 1

maxPer = 20 # can also be specified between 0 and 1

obj = alg.PFPGrowth(iFile, minSup, maxPer)

obj.mine()

periodicFrequentPatterns = obj.getPatterns()

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

obj.save("periodicFrequentPatterns")

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 and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

getMemoryRSS() → float[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() → float[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() → Dict[str, Tuple[int, int]][source]

Function to send the set of periodic-frequent patterns after completion of the mining process

Returns:

returning periodic-frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame[source]

Storing final periodic-frequent patterns in a dataframe

Returns:

returning periodic-frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float[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() → None[source]

Mining process will start from this function

Returns:

None

printResults() → None[source]

This function is used to print the results

Returns:

None

save(outFile: str) → None[source]

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

Parameters:

outFile (csv file) – name of the output file

Returns:

None

startMine() → None[source]

Code for the mining process will start from this function