PAMI.stablePeriodicFrequentPattern.basic package

Submodules

PAMI.stablePeriodicFrequentPattern.basic.SPPEclat module

class PAMI.stablePeriodicFrequentPattern.basic.SPPEclat.SPPEclat(inputFile, minSup, maxPer, maxLa, sep='\t')[source]

Bases: _stablePeriodicFrequentPatterns

Description:

Stable periodic pattern mining aims to dicover all interesting patterns in a temporal database using three contraints minimum support, maximum period and maximum lability, that have support no less than the user-specified minimum support constraint and lability no greater than maximum lability.

Reference:

Fournier-Viger, P., Yang, P., Lin, J. C.-W., Kiran, U. (2019). Discovering Stable Periodic-Frequent Patterns in Transactional Data. Proc. 32nd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2019), Springer LNAI, pp. 230-244

Parameters:

  • iFile – str : Name of the Input file to mine complete set of stable periodic Frequent Pattern.

  • oFile – str : Name of the output file to store complete set of stable periodic Frequent Pattern.

  • 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

  • maxLa – float : minimum loss of a pattern

  • 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:
iFilefile

Name of the Input file or path of the input file

oFilefile

Name of the output file or path of the output file

minSupint 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. Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

maxPerint or float or str

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

maxLaint or float or str

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

sepstr

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

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

startTime:float

To record the start time of the mining process

endTime:float

To record the completion time of the mining process

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

itemSetCountint

it represents the total no of patterns

finalPatternsdict

it represents to store the patterns

tidListdict

stores the timestamps of an item

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 an 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()

Scan the database and store the items with their timestamps which are periodic frequent

calculateLa()

Calculates the support and period for a list of timestamps.

Generation()

Used to implement prefix class equivalence method to generate the periodic patterns recursively

Methods to execute code on terminal

Format:

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

Example usage:

(.venv) $ python3 basic.py sampleDB.txt patterns.txt 10.0 4.0 2.0


        .. note:: constraints will be considered in percentage of database transactions

Importing this algorithm into a python program

… code-block:: python

from PAMI.stablePeriodicFrequentPattern.basic import basic as alg

obj = alg.PFPECLAT(“../basic/sampleTDB.txt”, 5, 3, 3)

obj.mine()

Patterns = obj.getPatterns()

print(“Total number of Stable Periodic Frequent Patterns:”, len(Patterns))

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]

Method to start the mining of patterns

getMemoryRSS()[source]

Total amount of RSS memory consumed by the mining process will be retrieved from this function :return: returning RSS memory consumed by the mining process :rtype: float

getMemoryUSS()[source]

Total amount of USS memory consumed by the mining process will be retrieved from this function :return: returning USS memory consumed by the mining process :rtype: float

getPatterns()[source]

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

Returns:

returning stable periodic-frequent patterns

Return type:

dict

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

printResults()[source]

This function is used to print the results

save(outFile)[source]

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

Parameters:

outFile (csv file) – name of the output file

startMine()[source]

Method to start the mining of patterns

PAMI.stablePeriodicFrequentPattern.basic.SPPGrowth module

class PAMI.stablePeriodicFrequentPattern.basic.SPPGrowth.SPPGrowth(inputFile, minSup, maxPer, maxLa, sep='\t')[source]

Bases: object

Description:

Stable periodic pattern mining aims to dicover all interesting patterns in a temporal database using three contraints minimum support, maximum period and maximum lability, that have support no less than the user-specified minimum support constraint and lability no greater than maximum lability.

Reference:

Dao, H.N. et al. (2022). Towards Efficient Discovery of Stable Periodic Patterns in Big Columnar Temporal Databases. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_70

Parameters:

  • 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

  • minSup – str: Minimum number of frequent patterns to be included in the output file.

  • maxLa – float: Minimum number of …

  • maxPer – float: Maximum number of frequent patterns to be included in the output file.

  • 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:
iFilefile

Name of the Input file or path of the input file

oFilefile

Name of the output file or path of the output file

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. Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

maxPerint or float or str

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

maxLaint or float or str

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

sepstr

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

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

startTimefloat

To record the start time of the mining process

endTimefloat

To record the completion time of the mining process

Databaselist

To store the transactions of a database in list

mapSupportDictionary

To maintain the information of item and their frequency

lnoint

To represent the total no of transaction

treeclass

To represents the Tree class

itemSetCountint

To represents the total no of patterns

finalPatternsdict

To store the complete 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()

to convert the user specified value

Methods to execute code on terminal

Format:

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

Example usage :

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

Note

constraints will be considered in percentage of database transactions

Importing this algorithm into a python program

from PAMI.stablePeriodicFrequentPattern.basic import topk as alg

obj = alg.topk(iFile, minSup, maxPer, maxLa)

obj.startMine()

Patterns = obj.getPatterns()

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

obj.save(oFile)

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.

SPPList = {}
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 periodic-frequent patterns after completion of the mining process

Returns:

returning periodic-frequent patterns

Return type:

dict

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

printResults()[source]

This function is used to print the results

save(outFile)[source]

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

Parameters:

outFile (csv file) – name of the output file

startMine()[source]

Mining process will start from this function

PAMI.stablePeriodicFrequentPattern.basic.SPPGrowthDump module

PAMI.stablePeriodicFrequentPattern.basic.abstract module

Module contents