PAMI.weightedUncertainFrequentPattern.basic package
Submodules
PAMI.weightedUncertainFrequentPattern.basic.WUFIM module
- class PAMI.weightedUncertainFrequentPattern.basic.WUFIM.WUFIM(iFile, wFile, expSup, expWSup, sep='\t')[source]
Bases:
_weightedFrequentPatterns- Description:
It is one of the algorithm to discover weighted frequent patterns in a uncertain transactional database using PUF-Tree.
- Reference:
Efficient Mining of Weighted Frequent Itemsets in Uncertain Databases, In book: Machine Learning and Data Mining in Pattern Recognition Chun-Wei Jerry Lin, Wensheng Gan, Philippe Fournier Viger, Tzung-Pei Hong
- Parameters:
iFile – str : Name of the Input file to mine complete set of Weighted Uncertain Periodic Frequent Patterns
oFile – str : Name of the output file to store complete set of Weighted Uncertain Periodic 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.
wFile – str : This is a weighted file.
- Attributes:
- iFilefile
Name of the Input file or path of the input file
- wFilefile
Name of the Input file or path of the input file
- oFilefile
Name of the output file or path of the output file
- minSupfloat 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
- 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(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
Methods to execute code on terminal
Format: (.venv) $ python3 basic.py <inputFile> <outputFile> <minSup> Example Usage: (.venv) $ python3 basic.py sampleTDB.txt patterns.txt 3 .. note:: minSup will be considered in support count or frequency
Importing this algorithm into a python program
from PAMI.weightedUncertainFrequentPattern.basic import basic as alg obj = alg.basic(iFile, wFile, expSup, expWSup) 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)
- getMemoryRSS() float[source]
Total amount of RSS memory consumed by the mining process will be retrieved from this function :return: returning RSS memory consumed by the mining process :rtype: float
- getMemoryUSS() float[source]
Total amount of USS memory consumed by the mining process will be retrieved from this function :return: returning USS memory consumed by the mining process :rtype: float
- getPatterns() dict[source]
Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict
- getPatternsAsDataFrame() DataFrame[source]
Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame
- getRuntime() float[source]
Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float
- mine() None[source]
mine() method where the patterns are mined by constructing tree and remove the false patterns by counting the original support of a patternS