STEclat
- class PAMI.georeferencedPartialPeriodicPattern.basic.STEclat.STEclat(iFile, nFile, minPS, maxIAT, sep='\t')[source]
Bases:
_partialPeriodicSpatialPatterns- Description:
STEclat is one of the fundamental algorithm to discover georefereneced partial periodic-frequent patterns in a transactional database.
- Reference:
R. Uday Kiran, C. Saideep, K. Zettsu, M. Toyoda, M. Kitsuregawa and P. Krishna Reddy, “Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases,” 2019 IEEE International
Conference on Big Data (Big Data), 2019, pp. 233-238, doi: 10.1109/BigData47090.2019.9005693.
- Parameters:
iFile – str : Name of the Input file to mine complete set of Geo-referenced Partial Periodic patterns
oFile – str : Name of the output file to store complete set of Geo-referenced Partial Periodic patterns
minPS – int or float or str : The user can specify minPS either in count or proportion of database size. If the program detects the data type of minPS is integer, then it treats minPS is expressed in count. Otherwise, it will be treated as float.
maxIAT – int or float or str : The user can specify maxIAT either in count or proportion of database size. If the program detects the data type of maxIAT is integer, then it treats maxIAT is expressed in count. Otherwise, it will be treated as float.
nFile – str : Name of the input file to mine complete set of Geo-referenced Partial Periodic 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
Input file name or path of the input file
- nFile: str:
Name of Neighbourhood file name
- maxIAT: float or int or str
The user can specify maxIAT either in count or proportion of database size. If the program detects the data type of maxIAT is integer, then it treats maxIAT is expressed in count. Otherwise, it will be treated as float. Example: maxIAT=10 will be treated as integer, while maxIAT=10.0 will be treated as float
- minPS: float or int or str
The user can specify minPS either in count or proportion of database size. If the program detects the data type of minPS is integer, then it treats minPS is expressed in count. Otherwise, it will be treated as float. Example: minPS=10 will be treated as integer, while minPS=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.
- startTime:float
To record the start time of the mining process
- endTime:float
To record the completion time of the mining process
- finalPatterns: dict
Storing the complete set of patterns in a dictionary variable
- oFilestr
Name of the output file to store complete set of frequent patterns
- 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
- Databaselist
To store the complete set of transactions available in the input database/file
- 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
- getPatternsAsDataFrames()
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
- creatingItemSets(iFileName)
Storing the complete transactions of the database/input file in a database variable
- frequentOneItem()
Generating one frequent patterns
- convert(value):
To convert the given user specified value
- getNeighbourItems(keySet):
A function to get common neighbours of a itemSet
- mapNeighbours(file):
A function to map items to their neighbours
Executing the code on terminal :
Format: (.venv) $ python3 STEclat.py <inputFile> <outputFile> <neighbourFile> <minPS> <maxIAT> Example Usage: (.venv) $ python3 STEclat.py sampleTDB.txt output.txt sampleN.txt 0.2 0.5
Note
maxIAT & minPS will be considered in percentage of database transactions
Sample run of importing the code :
import PAMI.georeferencedPartialPeriodicPattern.STEclat as alg obj = alg.STEclat("sampleTDB.txt", "sampleN.txt", 3, 4) obj.mine() partialPeriodicSpatialPatterns = obj.getPatterns() print("Total number of Periodic Spatial Frequent Patterns:", len(partialPeriodicSpatialPatterns)) obj.save("outFile") 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.
- 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