PFPMC
- class PAMI.periodicFrequentPattern.basic.PFPMC.PFPMC(iFile, minSup, maxPer, sep='\t')[source]
Bases:
_periodicFrequentPatterns- Description:
PFPMC is the fundamental approach to mine the periodic-frequent patterns.
- Reference:
(has to be added)
- 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 – str: Controls the minimum number of transactions in which every item must appear in a database.
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:
- iFilefile
Name of the Input file or path of the input file
- oFilefile
Name of the output file or path of the output file
- minSupint 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
- maxPerint 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.
- 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
- startTime:float
To record the start time of the mining process
- endTime:float
To record the completion time of the mining process
- 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
- itemSetCountint
it represents the total no of patterns
- finalPatternsdict
it represents to store the patterns
- tidListdict
stores the timestamps of an item
- hashingdict
stores the patterns with their support to check for the closed property
- Methods:
- mine()
Mining process will start from here
- getPatterns()
Complete set of patterns will be retrieved with this function
- save(oFile)
Complete set of periodic-frequent patterns will be loaded in to an output file
- getPatternsAsDataFrame()
Complete set of periodic-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
- creatingOneItemSets()
Scan the database and store the items with their timestamps which are periodic frequent
- getPeriodAndSupport()
Calculates the support and period for a list of timestamps.
- Generation()
Used to implement prefix class equivalence method to generate the periodic patterns recursively
Methods to execute code on terminal
Format: (.venv) $ python3 PFPMC.py <inputFile> <outputFile> <minSup> <maxPer> Example usage: (.venv) $ python3 PFPMC.py sampleDB.txt patterns.txt 10.0 4.0 .. note:: minSup and maxPer will be considered in percentage of database transactions
Importing this algorithm into a python program
from PAMI.periodicFrequentPattern.basic import PFPMC as alg obj = alg.PFPMC("../basic/sampleTDB.txt", "2", "5") obj.startMine() periodicFrequentPatterns = obj.getPatterns() print("Total number of Periodic 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() 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[source]
Function to send the set of periodic-frequent patterns after completion of the mining process
- Returns:
returning periodic-frequent patterns
- Return type:
dict
- getPatternsAsDataFrame() DataFrame[source]
Storing final periodic-frequent patterns in a dataframe
- Returns:
returning periodic-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