PSGrowth

class PAMI.periodicFrequentPattern.basic.PSGrowth.Node(item, children)[source]

Bases: object

A class used to represent the node of frequentPatternTree

Attributes:

itemint: storing item of a node
timeStampslist: To maintain the timeStamps of Database at the end of the branch
parentnode: To maintain the parent of every node
childrenlist: To maintain the children of node

Methods:

addChild(itemName): storing the children to their respective parent nodes

addChild(node) → None[source]

Appends the children node details to a parent node

Parameters:: node – children node
Returns:: appending children node to parent node

class PAMI.periodicFrequentPattern.basic.PSGrowth.PSGrowth(iFile, minSup, maxPer, sep='\t')[source]

Bases: _periodicFrequentPatterns

Description:: PS-Growth is one of the fundamental algorithm to discover periodic-frequent patterns in a temporal database.

:ReferenceA. Anirudh, R. U. Kiran, P. K. Reddy and M. Kitsuregaway, “Memory efficient mining of periodic-frequent: patterns in transactional databases,” 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, pp. 1-8, https://doi.org/10.1109/SSCI.2016.7849926

Parameters:

iFile – str : Name of the Input file to mine complete set of periodic frequent pattern’s
oFile – str : Name of the output file to store complete set of periodic frequent pattern’s
minSup – str: Controls the minimum number of transactions in which every item must appear in a database.
maxPer – str: Controls the maximum number of transactions in which any two items within a pattern can reappear.
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
maxPer: int 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
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.
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 transaction
treeclass: it represents the Tree class
itemSetCountint: it represents the total no of patterns
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 periodic-frequent patterns will be loaded in to an output file
getConditionalPatternsInDataFrame(): 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
OneLengthItems(): Scans the dataset or dataframes and stores in list format
buildTree(): after updating the Databases ar added into the tree by setting root node as null

Methods to execute code on terminal

Format:

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

Example:

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

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

Importing this algorithm into a python program

from PAMI.periodicFrequentPattern.basic import PSGrowth as alg

obj = alg.PSGrowth("../basic/sampleTDB.txt", "2", "6")

obj.startMine()

periodicFrequentPatterns = obj.getPatterns()

print("Total number of  Patterns:", len(periodicFrequentPatterns))

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() → None[source]: Mining process will start from this function :return: None

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[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() → DataFrame[source]

Storing final periodic-frequent patterns in a dataframe

Returns:: returning periodic-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

printResults() → None[source]: This function is used to print the results :return: None

save(outFile: str) → None[source]

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

Parameters:: outFile (csv file) – name of the output file
Returns:: None

startMine() → None[source]: Mining process will start from this function :return: None

PAMI.periodicFrequentPattern.basic.PSGrowth.conditionalTransactions(patterns, timestamp) → Tuple[List[List[int]], List[List[_Interval]], Dict[int, Tuple[int, int]]][source]

To sort and update the conditional transactions by removing the items which fails frequency and periodicity conditions

Parameters:

patterns – conditional patterns of a node
timestamp – timeStamps of a conditional pattern

Returns:

conditional transactions with their respective timeStamps

PAMI.periodicFrequentPattern.basic.PSGrowth.getPeriodAndSupport(timeStamps) → List[int][source]

Calculates the period and support of list of timeStamps

Parameters:: timeStamps – timeStamps of a pattern or item
Returns:: support and periodicity