k3PMiner
- class PAMI.partialPeriodicPattern.topk.k3PMiner.k3PMiner(iFile, k, period, sep='\t')[source]
Bases:
partialPeriodicPatterns- Description:
k3PMiner is and algorithm to discover top - k partial periodic patterns in a temporal database.
- Reference:
Palla Likhitha,Rage Uday Kiran, Discovering Top-K Partial Periodic Patterns in Big Temporal Databases https://dl.acm.org/doi/10.1007/978-3-031-39847-6_28
- Parameters:
iFile – str : Name of the Input file to mine complete set of periodic frequent pattern’s
oFile – str : Name of the output file to store complete set of periodic frequent pattern’s
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.
iFile – str : Name of the Input file to mine complete set of frequent pattern’s
oFile – str : Name of the output file to store complete set of frequent patterns
period – str: Minimum partial periodic…
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:
- iFilestr
Input file name or path of the input file
- k: int
User specified count of top partial periodic patterns
- 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.
- oFilestr
Name of the output file or the path of the output file
- 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
- 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
- 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()
Scans the dataset or dataframes and stores in list format
- frequentOneItem()
Generates one frequent patterns
- eclatGeneration(candidateList)
It will generate the combinations of frequent items
- generateFrequentPatterns(tidList)
It will generate the combinations of frequent items from a list of items
Executing the code on terminal:
Format: python3 k3PMiner.py <iFile> <oFile> <k> <period> Examples: python3 k3PMiner.py sampleDB.txt patterns.txt 10 3
Sample run of the importing code:
… code-block:: python
import PAMI.partialPeriodicPattern.topk.k3PMiner as alg
obj = alg.Topk_PPPGrowth(iFile, k, period)
obj.mine()
partialPeriodicPatterns = obj.getPatterns()
print(“Total number of top partial periodic Patterns:”, len(partialPeriodicPatterns))
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