FSPGrowth
- class PAMI.georeferencedFrequentPattern.basic.FSPGrowth.FSPGrowth(iFile, nFile, minSup, sep='\t')[source]
Bases:
_spatialFrequentPatterns- Description:
Given a transactional database and a spatial (or neighborhood) file, FSPM aims to discover all of those patterns that satisfy the user-specified minimum support (minSup) and maximum distance (maxDist) constraints
- Reference:
Rage, Uday & Fournier Viger, Philippe & Zettsu, Koji & Toyoda, Masashi & Kitsuregawa, Masaru. (2020). Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases.
- Parameters:
iFile – str : Name of the Input file to mine complete set of Geo-referenced frequent patterns
oFile – str : Name of the output file to store complete set of Geo-referenced 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.
maxPer – float : The user can specify maxPer in count or proportion of database size. If the program detects the data type of maxPer is integer, then it treats maxPer is expressed in count.
nFile – str : Name of the input file to mine complete set of Geo-referenced frequent patterns
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:
- iFilefile
Input file name or path of the input file
- nFilefile
Neighbourhood file name or path of the neighbourhood file
- oFilefile
Name of the output file or the path of output file
- minSupfloat
The user can specify minSup either in count or proportion of database size.
- finalPatternsdict
Storing the complete set of patterns in a dictionary variable
- startTime:float
To record the start time of the mining process
- endTime:float
To record the completion time of the mining process
- 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()
This function starts pattern mining.
- 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
- getPatternsInDataFrame()
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
- getNeighbour(string)
This function changes string to tuple(neighbourhood).
- getFrequentItems(database)
This function create frequent items from database.
- genCondTransaction(transaction, rank)
This function generates conditional transaction for processing on each workers.
- getPartitionId(item)
This function generates partition id
- mapNeighbourToNumber(neighbour, rank)
This function maps neighbourhood to number. Because in this program, each item is mapped to number based on fpList so that it can be distributed. So the contents of neighbourhood must also be mapped to a number.
- createFPTree()
This function creates FPTree.
- getAllFrequentPatterns(data, fpList, ndata)
This function generates all frequent patterns
Executing the code on terminal :
Format: (.venv) $ python3 FSPGrowth.py <inputFile> <outputFile> <neighbourFile> <minSup> Example Usage: (.venv) $ python3 FSPGrowth.py sampleTDB.txt output.txt sampleN.txt 0.5
Note
minSup will be considered in percentage of database transactions
Sample run of importing the code :
from PAMI.georeferencedFrequentPattern.basic import FSPGrowth as alg obj = alg.FSPGrowth("sampleTDB.txt", "sampleN.txt", 5) obj.mine() spatialFrequentPatterns = obj.getPatterns() print("Total number of Spatial Frequent Patterns:", len(spatialFrequentPatterns)) obj.save("outFile") 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 Yudai Masu 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