PAMI.georeferencedFrequentPattern.basic package

Submodules

PAMI.georeferencedFrequentPattern.basic.FSPGrowth module

PAMI.georeferencedFrequentPattern.basic.SpatialECLAT module

class PAMI.georeferencedFrequentPattern.basic.SpatialECLAT.SpatialECLAT(iFile, nFile, minSup, sep='\t')[source]

Bases: _spatialFrequentPatterns

Description:

Spatial Eclat is a Extension of ECLAT algorithm,which stands for Equivalence Class Clustering and bottom-up Lattice Traversal.It is one of the popular methods of Association Rule mining. It is a more efficient and scalable version of the Apriori algorithm.

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:
iFilestr

Input file name or path of the input file

nFile: str

Name of Neighbourhood file name

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. Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

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

oFilestr

Name of the output file to store complete set of frequent patterns

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 complete set of transactions available in the input database/file

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(iFileName)

Storing the complete transactions of the database/input file in a database variable

frequentOneItem()

Generating one frequent patterns

dictKeysToInt(iList)

Converting dictionary keys to integer elements

eclatGeneration(cList)

It will generate the combinations of frequent items

generateSpatialFrequentPatterns(tidList)

It will generate the combinations of frequent items from a list of items

convert(value):

To convert the given user specified value

getNeighbourItems(keySet):

A function to get common neighbours of a itemSet

mapNeighbours(file):

A function to map items to their neighbours

Executing the code on terminal :

Format:

(.venv) $ python3 SpatialECLAT.py <inputFile> <outputFile> <neighbourFile> <minSup>

Example Usage:

(.venv) $ python3 SpatialECLAT.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 SpatialECLAT as alg

obj = alg.SpatialECLAT("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 B.Sai Chitra 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

mine()[source]

Frequent pattern mining process will start from here

printResults()[source]

This function is used to print the results

save(outFile)[source]

Complete set of frequent patterns will be loaded in to a output file

Parameters:

outFile (csv file) – name of the output file

startMine()[source]

Frequent pattern mining process will start from here

PAMI.georeferencedFrequentPattern.basic.abstract module

Module contents