PAMI.faultTolerantFrequentPattern.basic package

Submodules

PAMI.faultTolerantFrequentPattern.basic.FTApriori module

class PAMI.faultTolerantFrequentPattern.basic.FTApriori.FTApriori(iFile, minSup, itemSup, minLength, faultTolerance, sep='\t')[source]

Bases: _faultTolerantFrequentPatterns

Description:

FT-Apriori is one of the fundamental algorithm to discover fault-tolerant frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively.

Reference:

Pei, Jian & Tung, Anthony & Han, Jiawei. (2001). Fault-Tolerant Frequent Pattern Mining: Problems and Challenges.

Parameters:

  • iFile – str : Name of the Input file to mine complete set of fault Tolerant frequent patterns

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

  • minSup – float or int 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. Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

  • itemSup – int or float : Frequency of an item

  • minLength – int : minimum length of a pattern

  • faultTolerance – int

  • 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:
startTimefloat

To record the start time of the mining process

endTimefloat

To record the completion time of the mining process

finalPatternsdict

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

Databaselist

To store the transactions of a database in list

Methods to execute code on terminal

Format:

(.venv) $ python3 FTApriori.py <inputFile> <outputFile> <minSup> <itemSup> <minLength> <faultTolerance>

Example Usage:

(.venv) $ python3 FTApriori.py sampleDB.txt patterns.txt 10.0 3.0 3 1

Note

minSup will be considered in times of minSup and count of database transactions

Importing this algorithm into a python program

from PAMI.faultTolerantFrequentPattern.basic import FTApriori as alg

obj = alg.FTApriori(inputFile,minSup,itemSup,minLength,faultTolerance)

obj.mine()

patterns = obj.getPatterns()

print("Total number of fault-tolerant frequent patterns:",  len(patterns))

obj.save("outputFile")

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() → 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[Tuple[str, ...], int][source]

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame[source]

Storing final frequent patterns in a dataframe

Returns:

returning 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]

Fault-tolerant frequent pattern mining process will start from here

printResults() → None[source]

This is function is used to print the result

save(outFile) → None[source]

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

Parameters:

outFile (csvfile) – name of the output file

Returns:

None

startMine() → None[source]

Fault-tolerant frequent pattern mining process will start from here

PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth module

class PAMI.faultTolerantFrequentPattern.basic.FTFPGrowth.FTFPGrowth(iFile: str | DataFrame, minSup: int | float | str, itemSup: float, minLength: int, faultTolerance: int, sep: str = '\t')[source]

Bases: _faultTolerantFrequentPatterns

Description:

FPGrowth is one of the fundamental algorithm to discover frequent patterns in a transactional database. It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs downward closure property to reduce the search space effectively.

Reference:

Han, J., Pei, J., Yin, Y. et al. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery 8, 53–87 (2004). https://doi.org/10.1023

Parameters:

  • iFile – file : Name of the Input file to mine complete set of fault Tolerant frequent patterns

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

  • minSup – float or int 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. Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

:param 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.

Attributes:
startTime: float :

To record the start time of the mining process

endTime: float :

To record the completion time of the mining process

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

Databaselist

To store the transactions of a database in list

mapSupportDictionary

To maintain the information of item and their frequency

lnoint

it represents the total no of transactions

treeclass

it represents the Tree class

finalPatternsdict

it represents to store the 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 frequent patterns will be loaded in to an 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()

Extracts the one-frequent patterns from transactions

Executing the code on terminal:

Format:

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

Example Usage:

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

Note

minSup will be considered in times of minSup and count of database transactions

Sample run of the importing code:

from PAMI.faultTolerantFrequentPattern.basic import FTFPGrowth as alg

obj = alg.FTFPGrowth(inputFile,minSup,itemSup,minLength,faultTolerance)

obj.mine()

patterns = obj.getPatterns()

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

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() → 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, int][source]

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame[source]

Storing final frequent patterns in a dataframe

Returns:

returning 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]

Main program to start the operation

printResults() → None[source]

This function is used to print the results

save(outFile: str) → None[source]

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

Parameters:

outFile (csv file) – name of the output file

Returns:

None

startMine() → None[source]

Main program to start the operation

PAMI.faultTolerantFrequentPattern.basic.abstract module

Module contents