PAMI.frequentPattern.topk package

Submodules

PAMI.frequentPattern.topk.FAE module

class PAMI.frequentPattern.topk.FAE.FAE(iFile, k, sep='\t')

Bases: _frequentPatterns

About this algorithm

Description:

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

Reference:

Zhi-Hong Deng, Guo-Dong Fang: Mining Top-Rank-K Frequent Patterns: DOI: 10.1109/ICMLC.2007.4370261 · Source: IEEE Xplore https://ieeexplore.ieee.org/document/4370261

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.

• k (int) – User specified count of top frequent patterns.

• minimum (int) – Minimum number of frequent patterns to consider in analysis.

• 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.

Execution methods

Terminal command

Format:

(.venv) $ python3 FAE.py <inputFile> <outputFile> <K>

Example Usage:

(.venv) $ python3 FAE.py sampleDB.txt patterns.txt 10.0

Note

k will be considered as count of top frequent patterns to consider in analysis.

Calling from a python program

import PAMI.frequentPattern.topK.FAE as alg

iFile = 'sampleDB.txt'

K = 2

obj = alg.FAE(iFile, K)

obj.mine()

topKFrequentPatterns = obj.getPatterns()

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

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

getMemoryRSS()

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()

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()

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime()

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()

TopK Frequent pattern mining process will start from here

printResults()

This function is used to print the results

save(outFile)

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

Parameters:

outFile (csvfile) – name of the output file

startMine()

TopK Frequent pattern mining process will start from here

PAMI.frequentPattern.topk.abstract module

Module contents