TubeS

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

To calculate the second probability of a node in transaction

Parameters:

• transaction – transaction in a database

• i – index of item in transaction

Returns:

second probability of a node

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

Bases: _frequentPatterns

About this algorithm

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

Execution methods

Terminal command

Format:

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

Example Usage:

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

Note

minSup can be specified in support count or a value between 0 and 1.

Calling from a python program

from PAMI.uncertainFrequentPattern.basic import TubeS as alg

iFile = 'sampleDB.txt'

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

obj = alg.TubeS(iFile, minSup)

obj.mine()

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.

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()

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

oFile = ' '

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 (file) – name of the output file

startMine() → None

Code for the mining process will start from this function

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

Parameters:

root – root node of tree

Returns:

details of tree