TopkPFP
- class PAMI.periodicFrequentPattern.topk.TopkPFP.TopkPFP.TopkPFPGrowth(iFile, minSup, maxPer, sep='\t')[source]
Bases:
_periodicFrequentPatterns- Description:
Top - K is and algorithm to discover top periodic frequent patterns in a temporal database.
- Reference:
Komate Amphawan, Philippe Lenca, Athasit Surarerks: “Mining Top-K Periodic-Frequent Pattern from Transactional Databases without Support Threshold” International Conference on Advances in Information Technology: https://link.springer.com/chapter/10.1007/978-3-642-10392-6_3
- param iFile:
str : Name of the Input file to mine complete set of periodic frequent pattern’s
- param oFile:
str : Name of the output file to store complete set of periodic frequent pattern’s
- param maxPer:
str: Controls the maximum number of transactions in which any two items within a pattern can reappear.
- param 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
- k: int
User specified counte of top frequent patterns
- 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.
- oFilestr
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
- 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 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()
Generates one frequent patterns
- eclatGeneration(candidateList)
It will generate the combinations of frequent items
- generateFrequentPatterns(tidList)
It will generate the combinations of frequent items from a list of items
Format: (.venv) $ python3 TopkPFP.py <inputFile> <outputFile> <k> <maxPer> Examples: (.venv) $ python3 TopkPFP.py sampleDB.txt patterns.txt 10 3 **Sample run of the importing code:** --------------------------------------- .. code-block:: python import PAMI.periodicFrequentPattern.topk.TopkPFPGrowth as alg obj = alg.TopkPFPGrowth(iFile, k, maxPer) obj.mine() periodicFrequentPatterns = obj.getPatterns() print("Total number of Frequent Patterns:", len(periodicFrequentPatterns)) obj.save(oFile) Df = obj.getPatternInDataFrame() 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