PPF_DFS
- class PAMI.partialPeriodicFrequentPattern.basic.PPF_DFS.PPF_DFS(iFile, minSup, maxPer, minPR, sep='\t')[source]
Bases:
partialPeriodicPatterns,ABCAbout this algorithm
- Description:
PPF_DFS is algorithm to mine the partial periodic frequent patterns.
- References:
(Has to be added)
- Parameters:
iFile (str) – Name of the Input file to mine complete set of correlated patterns.
oFile (str) – Name of the output file to store complete set of correlated 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.
minPR (str) – Controls the maximum number of transactions in which any two items within a pattern can reappear.
maxPer (str) – 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:
memoryUSS (float) – To store the total amount of USS memory consumed by the program.
memoryRSS (float) – 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.
minSup (int) – The user given minSup.
maxPer (int) – The user given maxPer.
minPR (int) – The user given minPR.
finalPatterns (dict) – It represents to store the pattern.
- Methods:
mine() – Mining process will start from here.
Generation(prefix, itemsets, tidsets) – Used to implement prefix class equibalence method to generate the periodic patterns recursively.
getPartialPeriodicPatterns() – Complete set of patterns will be retrieved with this function.
storePatternsInFile(ouputFile) – Complete set of frequent patterns will be loaded in to an output file.
getPatternsAsDataFrame() – Complete set of frequent patterns will be loaded in to an output file.
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.
Execution methods
Terminal command
Format: (.venv) $ python3 PPF_DFS.py <inputFile> <outputFile> <minSup> <maxPer> <minPR> Example Usage: (.venv) $ python3 PPF_DFS.py sampleTDB.txt output.txt 0.25 300 0.7
Note
minSup can be specified in support count or a value between 0 and 1.
Calling from a python program
from PAMI.partialPeriodicFrequentPattern.basic import PPF_DFS as alg iFile = 'sampleTDB.txt' minSup = 0.25 # can be specified between 0 and 1 maxPer = 300 # can be specified between 0 and 1 minPR = 0.7 # can be specified between 0 and 1 obj = alg.PPF_DFS(inputFile, minSup, maxPer, minPR, sep) obj.mine() partialPeriodicFrequentPatterns = obj.getPatterns() print("Total number of partial periodic Patterns:", len(partialPeriodicFrequentPatterns)) obj.save(oFile) Df = obj.getPatternInDf() 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 Nakamura and revised by Tarun Sreepada 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
- mine()[source]
Main program start with extracting the periodic frequent items from the database and performs prefix equivalence to form the combinations and generates closed periodic frequent patterns.
- oFile = None