UPGrowth

class PAMI.highUtilityPattern.basic.UPGrowth.UPGrowth(iFile: str, minUtil: int, sep: str = '\t')[source]

Bases: _utilityPatterns

Description:

UP-Growth is two-phase algorithm to mine High Utility Itemsets from transactional databases.

Reference:

Vincent S. Tseng, Cheng-Wei Wu, Bai-En Shie, and Philip S. Yu. 2010. UP-Growth: an efficient algorithm for high utility itemset mining. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ‘10). Association for Computing Machinery, New York, NY, USA, 253–262. DOI:https://doi.org/10.1145/1835804.1835839

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 frequent patterns

oFilefile

Name of the output file to store complete set of frequent 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

NumberOfNodesint

Total number of nodes generated while building the tree

ParentNumberOfNodesint

Total number of nodes required to build the parent tree

MapItemToMinimumUtilitymap

A map to store the minimum utility of item in the database

phuislist

A list to store the phuis

MapItemToTwumap

A map to store the twu of each item in database

Methods:
mine()

Mining process will start from here

getPatterns()

Complete set of patterns will be retrieved with this function

createLocalTree(tree, item)

A Method to Construct conditional pattern base

UPGrowth( tree, alpha)

A Method to Mine UP Tree recursively

PrintStats()

A Method to print number of phuis

save(oFile)

Complete set of frequent patterns will be loaded in to an 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

Executing the code on terminal:

Format:

(.venv) $ python3 UPGrowth <inputFile> <outputFile> <Neighbours> <minUtil> <sep>

Example Usage:

(.venv) $ python3 UPGrowth sampleTDB.txt output.txt sampleN.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 UPGrowth as alg

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

obj.mine()

highUtilityPattern = obj.getPatterns()

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

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.

PrintStats() None[source]

A Method to print number of phuis :return: None

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 frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame() DataFrame[source]

Storing final frequent patterns in a dataframe :return: returning frequent 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]

Mining process will start from here :return: None

printResults() None[source]

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

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

startMine() None[source]

Mining process will start from here :return: None