PAMI.weightedFrequentPattern.basic package
Submodules
PAMI.weightedFrequentPattern.basic.WFIM module
- class PAMI.weightedFrequentPattern.basic.WFIM.WFIM(iFile: str, wFile: str, minSup: str, minWeight: int, sep: str = '\t')[source]
Bases:
_weightedFrequentPatterns- Description:
WFMiner is one of the fundamental algorithm to discover weighted frequent patterns in a transactional database.
It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs employs downward closure property to reduce the search space effectively.
- :Reference :
U. Yun and J. J. Leggett, “Wfim: weighted frequent itemset mining with a weight range and a minimum weight,” in Proceedings of the 2005 SIAM International Conference on Data Mining. SIAM, 2005, pp. 636–640. https://epubs.siam.org/doi/pdf/10.1137/1.9781611972757.76
- Parameters:
iFile – str : Name of the Input file to mine complete set of weighted Frequent Patterns.
oFile – str : Name of the output file to store complete set of weighted Frequent Patterns.
minSup – str or int or float: minimum support thresholds were tuned to find the appropriate ranges in the limited memory
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
Input file name or path of the input file
- minSup: float or int 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
- minWeight: float or int or str
The user can specify minWeight either in count or proportion of database size. If the program detects the data type of minWeight is integer, then it treats minWeight is expressed in count. Otherwise, it will be treated as float. Example: minWeight=10 will be treated as integer, while minWeight=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.
- oFilefile
Name of the output file or the path of the output file
- startTime:float
To record the start time of the mining process
- endTime:float
To record the completion time of the mining process
- 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 transactions of a database in list
- mapSupportDictionary
To maintain the information of item and their frequency
- lnoint
it represents the total no of transactions
- treeclass
it represents the Tree class
- finalPatternsdict
it represents to store the patterns
: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
- creatingItemSets()
Scans the dataset or dataframes and stores in list format
- frequentOneItem()
Extracts the one-frequent patterns from transactions
Methods to execute code on terminal
Format: (.venv) $ python3 basic.py <inputFile> <weightFile> <outputFile> <minSup> <minWeight> Example Usage: (.venv) $ python3 basic.py sampleDB.txt weightSample.txt patterns.txt 10.0 3.4 .. note:: minSup and maxPer will be considered in support count or frequency
Importing this algorithm into a python program
from PAMI.weightFrequentPattern.basic import basic as alg obj = alg.basic(iFile, wFile, minSup, minWeight) obj.startMine() frequentPatterns = obj.getPatterns() print("Total number of Frequent Patterns:", len(frequentPatterns)) obj.savePatterns(oFile) 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() 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, int][source]
Function to send the set of frequent patterns after completion of the mining process.
- Returns:
returning frequent patterns
- Return type:
dict
- getPatternsAsDataFrame() DataFrame[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