HDSHUIM
- class PAMI.highUtilitySpatialPattern.basic.HDSHUIM.HDSHUIM(iFile: str, nFile: str, minUtil: int, sep: str = '\t')[source]
Bases:
_utilityPatterns- Description:
Spatial High Utility ItemSet Mining (SHUIM) [3] is an important model in data mining with many real-world applications. It involves finding all spatially interesting itemSets having high value in a quantitative spatio temporal database.
- Reference:
P. Pallikila et al., “Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases,” 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 4925-4935, doi: 10.1109/BigData52589.2021.9671912.
- Parameters:
iFile – str : Name of the Input file to mine complete set of High Utility Spatial patterns
oFile – str : Name of the output file to store complete set of High Utility Spatial patterns
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.
minUtil – int : Minimum utility threshold given by User
nFile – str : Name of the input file to mine complete set of High Utility Spatial patterns
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:
- iFilestr
Name of the input file to mine complete set of frequent patterns
- oFilestr
Name of the output file to store complete set of frequent patterns
- nFile: str
Name of Neighbourhood items file
- 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 generated
- huiCnt: int
huis created
- neighbors: map
keep track of neighbours of elements
- mapOfPMU: map
a map to keep track of Probable Maximum utility(PMU) of each item
- 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
- constructCUL(x, compactUList, st, minUtil, length, exNeighbours)
A method to construct CUL’s database
- 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, exNeighbours, minUtil)
A method to find all high utility itemSets
- updateClosed(x, compactUList, st, exCul, newT, ex, eyTs, length)
A method to update closed values
- saveItemSet(prefix, prefixLen, item, utility)
A method to save itemSets
- updateElement(z, compactUList, st, exCul, newT, ex, duPrevPos, eyTs)
A method to updates vales for duplicates
Executing the code on terminal:
Format: (.venv) $ python3 HDSHUIM.py <inputFile> <outputFile> <Neighbours> <minUtil> <separator> Example Usage: (.venv) $ python3 HDSHUIM.py sampleTDB.txt output.txt sampleN.txt 35 ','
Note
minSup will be considered in percentage of database transactions
Sample run of importing the code:
from PAMI.highUtilityGeoreferencedFrequentPattern.basic import HDSHUIM as alg obj=alg.HDSHUIM("input.txt","Neighbours.txt",35) obj.mine() Patterns = obj.getPatterns() print("Total number of Spatial 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() 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[str, str][source]
Function to send the set of frequent patterns after completion of the mining process
- Returns:
returning frequent patterns
- Return type:
dict
- getPatternsAsDataFrame() Dict[str, str][source]
Storing final frequent patterns in a dataframe
- Returns:
returning 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