PAMI.periodicFrequentPattern.closed package
Submodules
PAMI.periodicFrequentPattern.closed.CPFPMiner module
- class PAMI.periodicFrequentPattern.closed.CPFPMiner.CPFPMiner(iFile, minSup, maxPer, sep='\t')[source]
Bases:
_periodicFrequentPatterns- Description:
CPFPMiner algorithm is used to discover the closed periodic frequent patterns in temporal databases. It uses depth-first search.
- Reference:
P. Likhitha et al., “Discovering Closed Periodic-Frequent Patterns in Very Large Temporal Databases” 2020 IEEE International Conference on Big Data (Big Data), 2020, https://ieeexplore.ieee.org/document/9378215
- Parameters:
iFile – str : Name of the Input file to mine complete set of periodic frequent pattern’s
oFile – str : Name of the output file to store complete set of periodic frequent pattern’s
minSup – float: Controls the minimum number of transactions in which every item must appear in a database.
maxPer – float: Controls the maximum number of transactions in which any two items within a pattern can reappear.
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
- oFilestr
Name of the output file or path of the input file
- 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. Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float
- maxPer: int or float or str
The user can specify maxPer either 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. Otherwise, it will be treated as float. Example: maxPer=10 will be treated as integer, while maxPer=10.0 will be treated as float
- sepstr
This variable is used to distinguish items from one another in a transaction. The default seperator 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
- 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
- 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 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
Methods to execute code on terminal
Format: (.venv) $ python3 CPFPMiner.py <inputFile> <outputFile> <minSup> <maxPer> Example: (.venv) $ python3 CPFPMiner.py sampleTDB.txt patterns.txt 0.3 0.4 .. note:: minSup will be considered in percentage of database transactions
Importing this algorithm into a python program
from PAMI.periodicFrequentPattern.closed import CPFPMiner as alg obj = alg.CPFPMiner("../basic/sampleTDB.txt", "2", "6") obj.startMine() periodicFrequentPatterns = obj.getPatterns() print("Total number of Frequent Patterns:", len(periodicFrequentPatterns)) obj.save("patterns") Df = obj.getPatternsAsDataFrame() 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
- Returns:
returning RSS memory consumed by the mining process
- Return type:
float
- getMemoryUSS()[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()[source]
Function to send the set of frequent patterns after completion of the mining process
- Returns:
returning frequent patterns
- Return type:
dict
- getPatternsAsDataFrame()[source]
Storing final frequent patterns in a dataframe
- Returns:
returning frequent patterns in a dataframe
- Return type:
pd.DataFrame
- getRuntime()[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