PAMI.uncertainFrequentPattern.basic package

Submodules

PAMI.uncertainFrequentPattern.basic.CUFPTree module

class PAMI.uncertainFrequentPattern.basic.CUFPTree.CUFPTree(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

It is one of the fundamental algorithm to discover frequent patterns in a uncertain transactional database using CUFP-Tree.

Reference:

Chun-Wei Lin Tzung-PeiHong, ‘new mining approach for uncertain databases using CUFP trees’, Expert Systems with Applications, Volume 39, Issue 4, March 2012, Pages 4084-4093, https://doi.org/10.1016/j.eswa.2011.09.087

Parameters:

• iFile – str : Name of the Input file to mine complete set of Uncertain Frequent Patterns

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

• minSup – int or float or str : minimum support thresholds were tuned to find the appropriate ranges in the limited memory

• 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: 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

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

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 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

creatingItemSets(fileName)

Scans the dataset and stores in a list format

frequentOneItem()

Extracts the one-length frequent patterns from database

updateTransactions()

Update the transactions by removing non-frequent 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

mine()

Mining process will start from this function

Methods to execute code on terminal

Format:

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

Example Usage:

(.venv) $ python3 CUFPTree.py sampleTDB.txt patterns.txt 3



        .. note:: minSup  will be considered in support count or frequency

Importing this algorithm into a python program

from PAMI.uncertainFrequentPattern.basic import CUFPTree as alg

obj = alg.CUFPTree(iFile, minSup)v

obj.startMine()

frequentPatterns = obj.getPatterns()

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

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.

Mine() → None

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns. :return: None

getMemoryRSS() → float

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

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

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float

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() → None

This function is used to print the results :return: None

save(outFile: str) → None

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

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns. :return: None

PAMI.uncertainFrequentPattern.basic.PUFGrowth module

class PAMI.uncertainFrequentPattern.basic.PUFGrowth.PUFGrowth(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

It is one of the fundamental algorithm to discover frequent patterns in a uncertain transactional database using PUF-Tree.

Reference:

Carson Kai-Sang Leung, Syed Khairuzzaman Tanbeer, “PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data”, Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD 2013), https://link.springer.com/chapter/10.1007/978-3-642-37453-1_2

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

minSupfloat 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

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 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

creatingItemSets(fileName)

Scans the dataset and stores in a list format

frequentOneItem()

Extracts the one-length frequent patterns from database

updateTransactions()

Update the transactions by removing non-frequent 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

mine()

Mining process will start from this function

Methods to execute code on terminal

Format:

>>> python3 PUFGrowth.py <inputFile> <outputFile> <minSup>

Example:

>>>  python3 PUFGrowth.py sampleTDB.txt patterns.txt 3

Note

minSup will be considered in support count or frequency

Importing this algorithm into a python program

Credits:

The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.

getMemoryRSS() → float

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() → float

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() → dict

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame

getRuntime() → float

Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float

printResults() → None

This function is used to print the results

save(outFile: str) → None

Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the output file :type outFile: csv file

startMine() → None

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

PAMI.uncertainFrequentPattern.basic.TUFP module

class PAMI.uncertainFrequentPattern.basic.TUFP.TUFP(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

It is one of the fundamental algorithm to discover top-k frequent patterns in a uncertain transactional database using CUP-Lists.

Reference:

Tuong Le, Bay Vo, Van-Nam Huynh, Ngoc Thanh Nguyen, Sung Wook Baik 5, “Mining top-k frequent patterns from uncertain databases”, Springer Science+Business Media, LLC, part of Springer Nature 2020, https://doi.org/10.1007/s10489-019-01622-1

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

minSupfloat 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

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

storePatternsInFile(oFile)

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

getPatternsInDataFrame()

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(fileName)

Scans the dataset and stores in a list format

frequentOneItem()

Extracts the one-length frequent patterns from database

updateTransactions()

Update the transactions by removing non-frequent 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

mine()

Mining process will start from this function

Methods to execute code on terminal

Format:

>>> python3 TUFP.py <inputFile> <outputFile> <minSup>

Example:

>>>  python3 TUFP.py sampleTDB.txt patterns.txt 0.6
.. note:: minSup  will be considered in support count or frequency

Importing this algorithm into a python program

Credits:

The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.

getMemoryRSS() → float

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() → float

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() → Dict[str, float]

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame

getRuntime() → float

Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float

printResults() → None

This function is used to print the results

save(outFile: str) → None

Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the output file :type outFile: file

startMine() → None

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

PAMI.uncertainFrequentPattern.basic.TubeP module

class PAMI.uncertainFrequentPattern.basic.TubeP.TUFP(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

It is one of the fundamental algorithm to discover top-k frequent patterns in a uncertain transactional database using CUP-Lists.

Reference:

Tuong Le, Bay Vo, Van-Nam Huynh, Ngoc Thanh Nguyen, Sung Wook Baik 5, “Mining top-k frequent patterns from uncertain databases”, Springer Science+Business Media, LLC, part of Springer Nature 2020, https://doi.org/10.1007/s10489-019-01622-1

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

minSupfloat 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

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

storePatternsInFile(oFile)

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

getPatternsInDataFrame()

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(fileName)

Scans the dataset and stores in a list format

frequentOneItem()

Extracts the one-length frequent patterns from database

updateTransactions()

Update the transactions by removing non-frequent 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

mine()

Mining process will start from this function

Methods to execute code on terminal

Format:

>>> python3 TUFP.py <inputFile> <outputFile> <minSup>

Example:

>>>  python3 TUFP.py sampleTDB.txt patterns.txt 0.6
.. note:: minSup  will be considered in support count or frequency

Importing this algorithm into a python program

Credits:

The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.

getMemoryRSS() → float

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() → float

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() → Dict[str, float]

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame

getRuntime() → float

Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float

printResults() → None

This function is used to print the results

save(outFile: str) → None

Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the output file :type outFile: file

startMine() → None

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

PAMI.uncertainFrequentPattern.basic.TubeS module

PAMI.uncertainFrequentPattern.basic.TubeS.Second(transaction, i)

To calculate the second probability of a node in transaction :param transaction: transaction in a database :param i: index of item in transaction :return: second probability of a node

class PAMI.uncertainFrequentPattern.basic.TubeS.TubeS(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

TubeS is one of the fastest algorithm to discover frequent patterns in a uncertain transactional database.

Reference:

Carson Kai-Sang Leung and Richard Kyle MacKinnon. 2014. Fast Algorithms for Frequent Itemset Mining from Uncertain Data. In Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM ‘14). IEEE Computer Society, USA, 893–898. https://doi.org/10.1109/ICDM.2014.146

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

minSupfloat 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

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 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

creatingItemSets(fileName)

Scans the dataset and stores in a list format

frequentOneItem()

Extracts the one-length frequent patterns from database

updateTransactions()

Update the transactions by removing non-frequent 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:

>>> python3 TubeS.py <inputFile> <outputFile> <minSup>

Example:

>>>  python3 TubeS.py sampleTDB.txt patterns.txt 3

Note

minSup will be considered in support count or frequency

Importing this algorithm into a python program

Credits:

The complete program was written by P.Likhitha 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 :return: returning RSS memory consumed by the mining process :rtype: float

getMemoryUSS()

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()

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame

getRuntime()

Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float

printResults()

This function is used to print the results

save(outFile)

Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the output file :type outFile: file

startMine()

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

updateTransactions(dict1)

remove the items which are not frequent from transactions and updates the transactions with rank of items :param dict1 : frequent items with support :type dict1 : dictionary

PAMI.uncertainFrequentPattern.basic.TubeS.printTree(root)

To print the tree with root node through recursion :param root: root node of tree :return: details of tree

PAMI.uncertainFrequentPattern.basic.UFGrowth module

class PAMI.uncertainFrequentPattern.basic.UFGrowth.UFGrowth(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

It is one of the fundamental algorithm to discover frequent patterns in a uncertain transactional database using PUF-Tree.

Reference:

Carson Kai-Sang Leung, Syed Khairuzzaman Tanbeer, “PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data”, Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD 2013), https://link.springer.com/chapter/10.1007/978-3-642-37453-1_2

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

minSupfloat 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

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 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

creatingItemSets(fileName)

Scans the dataset and stores in a list format

frequentOneItem()

Extracts the one-length frequent patterns from database

updateTransactions()

Update the transactions by removing non-frequent 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

mine()

Mining process will start from this function

Methods to execute code on terminal

Format:

>>>  python3 PUFGrowth.py <inputFile> <outputFile> <minSup>

Example:

>>>  python3 PUFGrowth.py sampleTDB.txt patterns.txt 3
.. note:: minSup  will be considered in support count or frequency

Importing this algorithm into a python program

Credits:

The complete program was written by P.Likhitha 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 :return: returning RSS memory consumed by the mining process :rtype: float

getMemoryUSS()

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()

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame

getRuntime()

Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float

printResults()

This function is used to print the results

save(outFile)

Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the output file :type outFile: csv file

startMine()

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

PAMI.uncertainFrequentPattern.basic.UVECLAT module

class PAMI.uncertainFrequentPattern.basic.UVECLAT.UVEclat(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

It is one of the fundamental algorithm to discover frequent patterns in an uncertain transactional database using PUF-Tree.

Reference:

Carson Kai-Sang Leung, Lijing Sun: “Equivalence class transformation based mining of frequent itemsets from uncertain data”, SAC ‘11: Proceedings of the 2011 ACM Symposium on Applied ComputingMarch, 2011, Pages 983–984, https://doi.org/10.1145/1982185.1982399 :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

minSupfloat 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

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

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

storePatternsInFile(oFile)

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

getPatternsInDataFrame()

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(fileName)

Scans the dataset and stores in a list format

frequentOneItem()

Extracts the one-length frequent patterns from database

Methods to execute code on terminal

Format:

>>> python3 uveclat.py <inputFile> <outputFile> <minSup>

Example:

>>>  python3 uveclat.py sampleTDB.txt patterns.txt 3
.. note:: minSup  will be considered in support count or frequency

Importing this algorithm into a python program

Credits:

The complete program was written by P.Likhitha 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 :return: returning RSS memory consumed by the mining process :rtype: float

getMemoryUSS()

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()

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame

getRuntime()

Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float

printResults()

This function is used to print the results

save(oFile)

Complete set of frequent patterns will be loaded in to an output file :param oFile: name of the output file :type oFile: csv file

startMine()

Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns

PAMI.uncertainFrequentPattern.basic.abstract module

Module contents