FPGrowth
- class PAMI.frequentPattern.basic.FPGrowth.FPGrowth(iFile, minSup, sep='\t')[source]
Bases:
_frequentPatternsAbout this algorithm
- 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 or URL or dataFrame) – 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. Otherwise, it will be treated as float.
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:
startTime (float) – To record the start time of the mining process.
endTime (float) – To record the completion time of the mining process.
finalPatterns (dict) – Storing the complete set of patterns in a dictionary variable.
memoryUSS (float) – To store the total amount of USS memory consumed by the program.
memoryRSS (float) – To store the total amount of RSS memory consumed by the program.
Database (list) – To store the transactions of a database in list.
mapSupport (Dictionary) – To maintain the information of item and their frequency.
tree (class) – it represents the Tree class.
Execution methods
Terminal command
Format: (.venv) $ python3 FPGrowth.py <inputFile> <outputFile> <minSup> Example Usage: (.venv) $ python3 FPGrowth.py sampleDB.txt patterns.txt 10.0
Note
minSup can be specified in support count or a value between 0 and 1.
Calling from a python program
from PAMI.frequentPattern.basic import FPGrowth as alg iFile = 'sampleDB.txt' minSup = 10 # can also be specified between 0 and 1 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 and revised by Tarun Sreepada 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[str, int][source]
Function to send the set of frequent patterns after completion of the mining process
- Returns:
returning frequent patterns
- Return type:
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