FTApriori
- class PAMI.faultTolerantFrequentPattern.basic.FTApriori.FTApriori(iFile, minSup, itemSup, minLength, faultTolerance, sep='\t')[source]
Bases:
_faultTolerantFrequentPatterns- Description:
FT-Apriori is one of the fundamental algorithm to discover fault-tolerant frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively.
- Reference:
Pei, Jian & Tung, Anthony & Han, Jiawei. (2001). Fault-Tolerant Frequent Pattern Mining: Problems and Challenges.
- Parameters:
iFile – str : Name of the Input file to mine complete set of fault Tolerant frequent patterns
oFile – str : Name of the output file to store complete set of falut Tolerant frequent patterns
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
itemSup – int or float : Frequency of an item
minLength – int : minimum length of a pattern
faultTolerance – int
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
Methods to execute code on terminal
Format: (.venv) $ python3 FTApriori.py <inputFile> <outputFile> <minSup> <itemSup> <minLength> <faultTolerance> Example Usage: (.venv) $ python3 FTApriori.py sampleDB.txt patterns.txt 10.0 3.0 3 1
Note
minSup will be considered in times of minSup and count of database transactions
Importing this algorithm into a python program
from PAMI.faultTolerantFrequentPattern.basic import FTApriori as alg obj = alg.FTApriori(inputFile,minSup,itemSup,minLength,faultTolerance) obj.mine() patterns = obj.getPatterns() print("Total number of fault-tolerant frequent patterns:", len(patterns)) obj.save("outputFile") 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[Tuple[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