FAE
- class PAMI.frequentPattern.topk.FAE.FAE(iFile, k, sep='\t')[source]
Bases:
_frequentPatterns- Description:
Top - K is and algorithm to discover top frequent patterns in a transactional database.
- Reference:
Zhi-Hong Deng, Guo-Dong Fang: Mining Top-Rank-K Frequent Patterns: DOI: 10.1109/ICMLC.2007.4370261 · Source: IEEE Xplore https://ieeexplore.ieee.org/document/4370261
- 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
k – int : User specified count of top frequent patterns
minimum – int : Minimum number of frequent patterns to consider in analysis
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
- finalPatternsdict
it represents to store the patterns
Methods to execute code on terminal
Format: (.venv) $ python3 FAE.py <inputFile> <outputFile> <K> Example Usage: (.venv) $ python3 FAE.py sampleDB.txt patterns.txt 10
Note
k will be considered as count of top frequent patterns to consider in analysis
Importing this algorithm into a python program
import PAMI.frequentPattern.topK.FAE as alg obj = alg.FAE(iFile, K) obj.mine() topKFrequentPatterns = obj.getPatterns() print("Total number of Frequent Patterns:", len(topKFrequentPatterns)) obj.save(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()[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