HMiner
- class PAMI.highUtilityPattern.basic.HMiner.HMiner(iFile1, minUtil, sep='\t')[source]
Bases:
_utilityPatterns
- Description:
High Utility itemSet Mining (HMIER) is an importent algorithm to miner High utility items from the database.
- Reference:
- 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.
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.
maxPer – float : The user can specify maxPer 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.
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
- mapFMAP: list
EUCS map of the FHM algorithm
- candidates: int
candidates genetated
- huiCnt: int
huis created
- neighbors: map
keep track of nighboues of elements
- 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
- Explore_SearchTree(prefix, uList, minUtil)
A method to find all high utility itemSets
- UpdateCLosed(x, culs, st, excul, newT, ex, ey_ts, length)
A method to update closed values
- saveitemSet(prefix, prefixLen, item, utility)
A method to save itemSets
- updateElement(z, culs, st, excul, newT, ex, duppos, ey_ts)
A method to updates vales for duplicates
- construcCUL(x, culs, st, minUtil, length, exnighbors)
A method to construct CUL’s database
Executing the code on terminal:
Format: (.venv) $ python3 HMiner.py <inputFile> <outputFile> <minUtil> Example Usage: (.venv) $ python3 HMiner.py sampleTDB.txt output.txt 35
Note
minSup will be considered in percentage of database transactions
Sample run of importing the code:
from PAMI.highUtilityPattern.basic import HMiner as alg obj = alg.HMiner("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 B.Sai Chitra under the supervision of Professor Rage Uday Kiran.
- getMemoryRSS()[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()[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()[source]
Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict
- getPatternsAsDataFrame()[source]
Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame
- getRuntime()[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