EFIM

class PAMI.highUtilityPattern.basic.EFIM.EFIM(iFile, minUtil, sep='\t')[source]

Bases: _utilityPatterns

Description:

EFIM is one of the fastest algorithm to mine High Utility ItemSets from transactional databases.

Reference:

Zida, S., Fournier-Viger, P., Lin, J.CW. et al. EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowl Inf Syst 51, 595–625 (2017). https://doi.org/10.1007/s10115-016-0986-0

Parameters:

iFile – str : Name of the Input file to mine complete set of High Utility patterns
oFile – str : Name of the output file to store complete set of High Utility patterns
minUtil – int : The user given minUtil value.
candidateCount – int Number of candidates specified by user
maxMemory – int Maximum memory used by this program for running
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 to mine complete set of high utility patterns
oFilefile: Name of the output file to store complete set of high utility patterns
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
minUtilint: The user given minUtil value
highUtilityitemSets: map: set of high utility itemSets
candidateCount: int: Number of candidates
utilityBinArrayLU: list: A map to hold the local utility values of the items in database
utilityBinArraySU: list: A map to hold the subtree utility values of the items is database
oldNamesToNewNames: list: A map which contains old names, new names of items as key value pairs
newNamesToOldNames: list: A map which contains new names, old names of items as key value pairs
maxMemory: float: Maximum memory used by this program for running
patternCount: int: Number of HUI’s
itemsToKeep: list: keep only the promising items ie items having local utility values greater than or equal to minUtil
itemsToExplore: list: list of items that have subtreeUtility value greater than or equal to minUtil

:Methods :

mine()
Mining process will start from here

getPatterns()
Complete set of patterns will be retrieved with this function

save(oFile)
Complete set of patterns will be loaded in to a output file

getPatternsAsDataFrame()
Complete set of 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

backTrackingEFIM(transactionsOfP, itemsToKeep, itemsToExplore, prefixLength)
A method to mine the HUIs Recursively

useUtilityBinArraysToCalculateUpperBounds(transactionsPe, j, itemsToKeep)
A method to calculate the sub-tree utility and local utility of all items that can extend itemSet P and e

output(tempPosition, utility)
A method to output a high-utility itemSet to file or memory depending on what the user chose

is_equal(transaction1, transaction2)
A method to Check if two transaction are identical

useUtilityBinArrayToCalculateSubtreeUtilityFirstTime(dataset)
A method to calculate the sub tree utility values for single items

sortDatabase(self, transactions)
A Method to sort transaction

sort_transaction(self, trans1, trans2)
A Method to sort transaction

useUtilityBinArrayToCalculateLocalUtilityFirstTime(self, dataset)
A method to calculate local utility values for single itemsets

Executing the code on terminal:

Format:

(.venv) $ python3 EFIM.py <inputFile> <outputFile> <minUtil> <sep>

Example Usage:

(.venv) $ python3 EFIM sampleTDB.txt output.txt 35

Note

maxMemory will be considered as Maximum memory used by this program for running

Sample run of importing the code:

from PAMI.highUtilityPattern.basic import EFIM as alg

obj=alg.EFIM("input.txt",35)

obj.mine()

Patterns = obj.getPatterns()

print("Total number of high utility Patterns:", len(Patterns))

obj.save("output")

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 pradeep pallikila 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 :return: returning RSS memory consumed by the mining process :rtype: float

getMemoryUSS() → float[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() → dict[source]: Function to send the set of patterns after completion of the mining process :return: returning patterns :rtype: dict

getPatternsAsDataFrame() → _pd.DataFrame[source]: Storing final patterns in a dataframe :return: returning patterns in a dataframe :rtype: pd.DataFrame

getRuntime() → float[source]: Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float

mine() → None[source]: Start the EFIM algorithm. :return: None

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 :param outFile: name of the output file :type outFile: csv file :return: None

sort_transaction(trans1: _Transaction, trans2: _Transaction) → int[source]: A Method to sort transaction :param trans1: the first transaction :type trans1: Trans :param trans2:the second transaction :type trans2: Trans :return: sorted transaction :rtype: int

startMine() → None[source]: Start the EFIM algorithm. :return: None