PAMI.uncertainGeoreferencedFrequentPattern.basic package
Submodules
PAMI.uncertainGeoreferencedFrequentPattern.basic.GFPGrowth module
- class PAMI.uncertainGeoreferencedFrequentPattern.basic.GFPGrowth.GFPGrowth(iFile, nFile, minSup, sep='\t')[source]
Bases:
_frequentPatterns- Description:
GFPGrowth algorithm is used to discover geo-referenced frequent patterns in a uncertain transactional database using GFP-Tree.
- Reference:
Palla Likhitha,Pamalla Veena, Rage, Uday Kiran, Koji Zettsu (2023). “Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases”. PAKDD 2023. https://doi.org/10.1007/978-3-031-33380-4_3
- Parameters:
iFile – str : Name of the Input file to mine complete set of uncertain Geo referenced Frequent Patterns
oFile – str : Name of the output file to store complete set of Uncertain Geo referenced frequent patterns
minSup – str: 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
Name of the Input file or path of the input file
- oFilefile
Name of the output file or path of the output 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
- 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
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
- savePatterns(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(fileName)
Scans the dataset and stores in a list format
- frequentOneItem()
Extracts the one-length frequent patterns from database
- updateTransactions()
Update the transactions by removing non-frequent 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
- mine()
Mining process will start from this function
Executing the code on terminal:
Format: (.venv) $ python3 GFPGrowth.py <inputFile> <neighborFile> <outputFile> <minSup> Examples usage: (.venv) $ python3 GFPGrowth.py sampleTDB.txt sampleNeighbor.txt patterns.txt 3 .. note:: minSup will be considered in support count or frequency
Sample run of importing the code:
from PAMI.uncertainGeoreferencedFrequentPattern.basic import GFPGrowth as alg obj = alg.GFPGrowth(iFile, nFile, minSup) obj.startMine() Patterns = obj.getPatterns() print("Total number of Patterns:", len(Patterns)) 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.
- Mine()[source]
Main method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patterns
- 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