FPGrowth
- class PAMI.frequentPattern.basic.FPGrowth.FPGrowth(iFile, minSup, sep='\t')[source]
Bases:
_frequentPatterns- Description:
FPGrowth is one of the fundamental algorithm to discover frequent patterns in a transactional database. It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs downward closure property to reduce the search space effectively.
- Reference:
Han, J., Pei, J., Yin, Y. et al. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery 8, 53–87 (2004). https://doi.org/10.1023
- Parameters:
iFile – str : Name of the Input file to mine complete set of frequent patterns
oFile – str : Name of the output file to store complete set of frequent 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.
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:
- startTimefloat
To record the start time of the mining process
- endTimefloat
To record the completion time of the mining process
- finalPatternsdict
Storing the complete set of patterns in a dictionary variable
- 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
- 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
- finalPatternsdict
it represents to store the patterns
Methods to execute code on terminal
Format: (.venv) $ python3 FPGrowth.py <inputFile> <outputFile> <minSup> Example Usage: (.venv) $ python3 FPGrowth.py sampleDB.txt patterns.txt 10.0
Note
minSup will be considered in percentage of database transactions
Importing this algorithm into a python program
from PAMI.frequentPattern.basic import FPGrowth as alg obj = alg.FPGrowth(iFile, minSup) obj.mine() frequentPatterns = obj.getPatterns() print("Total number of Frequent Patterns:", len(frequentPatterns)) obj.savePatterns(oFile) Df = obj.getPatternInDataFrame() 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 :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
- Returns:
returning USS memory consumed by the mining process
- Return type:
float
- getPatterns() Dict[str, int][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
- Returns:
returning 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