PAMI.periodicCorrelatedPattern.basic package
Submodules
PAMI.periodicCorrelatedPattern.basic.EPCPGrowth module
- class PAMI.periodicCorrelatedPattern.basic.EPCPGrowth.EPCPGrowth(iFile, minSup, minAllConf, maxPer, maxPerAllConf, sep='\t')[source]
Bases:
_periodicCorrelatedPatterns- Description:
EPCPGrowth is an algorithm to discover periodic-Correlated patterns in a temporal database.
- Reference:
- 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
- minAllConfint or float or str
The user can specify minAllConf either in count or proportion of database size. If the program detects the data type of minAllConf is integer, then it treats minAllCOnf is expressed in count. Otherwise, it will be treated as float. Example: minAllCOnf=10 will be treated as integer, while minAllConf=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
- maxPerAllConfint or float or str
The user can specify maxPerAllConf either in count or proportion of database size. If the program detects the data type of maaxPerAllConf is integer, then it treats maxPerAllConf is expressed in count. Otherwise, it will be treated as float. Example : maxPerAllConf=10 will be treated as integer, while maxPerAllConf=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
- startTimefloat
To record the start time of the mining process
- endTimefloat
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
To represent the total no of transaction
- treeclass
To represents the Tree class
- itemSetCountint
To represents the total no of patterns
- finalPatternsdict
To store the complete 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 periodic-frequent patterns will be loaded in to a 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
- creatingItemSets(fileName)
Scans the dataset and stores in a list format
- PeriodicFrequentOneItem()
Extracts the one-periodic-frequent patterns from database
- updateDatabases()
Update the database by removing aperiodic items and sort the Database by item decreased support
- buildTree()
After updating the Database, remaining items will be added into the tree by setting root node as null
- convert()
to convert the user specified value
Executing the code on terminal:
- Format:
>>> python3 PFPGrowth.py <inputFile> <outputFile> <minSup> <maxPer>
- Examples:
>>> python3 PFPGrowth.py sampleTDB.txt patterns.txt 0.3 0.4
Sample run of importing the code:
from PAMI.periodicCorrelatedPattern.basic import EPCPGrowth as alg obj = alg.EPCPGrowth(iFile, minSup, minAllCOnf, maxPer, maxPerAllConf) obj.startMine() periodicCorrelatedPatterns = obj.getPatterns() print("Total number of Periodic Frequent Patterns:", len(periodicCorrelatedPatterns)) obj.save(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[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