PAMI.georeferencedFrequentPattern.basic package

Submodules

PAMI.georeferencedFrequentPattern.basic.FSPGrowth module

class PAMI.georeferencedFrequentPattern.basic.FSPGrowth.FSPGrowth(iFile, nFile, minSup, sep='\t')

Bases: _spatialFrequentPatterns

Description:

Given a transactional database and a spatial (or neighbourhood) file, FSPM aims to discover all of those patterns that satisfy the user-specified minimum support (minSup) and neighbourhood (maxDist) constraints. A pattern is spatially valid when all of its items are mutual neighbours.

This implementation preserves the public API of the original PAMI FSPGrowth class but replaces the earlier tree/prefix internals with a compact, parent-linked FP-tree, header-table mining, weighted conditional pattern bases, dictionary-based frequency ordering, and recursive spatial pruning that enforces the neighbourhood constraint during recursion rather than only at output time. It produces the same set of patterns and supports as the original.

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 or pd.DataFrame : Name/path of the input transactional database, or a DataFrame containing a ‘Transactions’ column.

• nFile – str or pd.DataFrame : Name/path of the input neighbourhood file, or a DataFrame containing item and neighbour columns.

• minSup – int or float or str : The user can specify minSup either as a count or as a proportion of the database size. If the data type of minSup is integer, it is treated as a count. Otherwise (float or decimal string), it is treated as a proportion of the number of transactions.

• sep – str : Separator used to distinguish items within a transaction. Default is a tab space; users may override.

Attributes:

iFilestr or pd.DataFrame

Input file name/path of the transactional database.

nFilestr or pd.DataFrame

Input file name/path of the neighbourhood file.

oFilestr

Name/path of the output file.

minSupint or float

Minimum support, as a count or as a proportion of database size.

finalPatternsdict

Complete set of discovered patterns: tab-joined pattern string -> support.

startTimefloat

Start time of the mining process.

endTimefloat

Completion time of the mining process.

memoryUSSfloat

Total USS memory consumed by the mining process.

memoryRSSfloat

Total RSS memory consumed by the mining process.

Methods:

mine()

Starts the pattern-mining process.

getPatterns()

Returns the complete set of patterns as a dictionary.

save(oFile)

Writes the complete set of patterns to an output file.

getPatternsAsDataFrame()

Returns the complete set of patterns as a pandas DataFrame.

getMemoryUSS()

Returns the total USS memory consumed by the mining process.

getMemoryRSS()

Returns the total RSS memory consumed by the mining process.

getRuntime()

Returns the total runtime taken by the mining process.

printResults()

Prints a summary of the results.

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

A float minSup is interpreted as a proportion of the 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 original program was written by Yudai Masu under the supervision of Professor Rage Uday Kiran. This optimized, API-compatible implementation retains that public interface while replacing the internal mining engine.

getMemoryRSS()

Total amount of RSS memory consumed by the mining process.

Returns:

RSS memory consumed.

Return type:

float

getMemoryUSS()

Total amount of USS memory consumed by the mining process.

Returns:

USS memory consumed.

Return type:

float

getPatterns()

Return the complete set of frequent patterns after mining.

Returns:

Patterns as { tab-joined pattern string : support }.

Return type:

dict

getPatternsAsDataFrame()

Store the discovered frequent spatial patterns in a DataFrame.

Returns:

Patterns as a DataFrame with columns [‘Patterns’, ‘Support’].

Return type:

pd.DataFrame

getRuntime()

Total runtime taken by the mining process.

Returns:

Runtime in seconds.

Return type:

float

mine()

Start the pattern-mining process.

printResults()

Print a summary of the results.

save(oFile)

Write the complete set of frequent patterns to an output file.

Parameters:

oFile (str) – Name of the output file.

startMine()

Start the pattern-mining process (deprecated alias for mine()).

class PAMI.georeferencedFrequentPattern.basic.FSPGrowth.SpatialFPGrowth(iFile, nFile, minSup, sep='\t')

Bases: FSPGrowth

PAMI.georeferencedFrequentPattern.basic.SpatialECLAT module

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

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

nFilestr

Name of Neighbourhood file name

minSupint 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

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

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

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()

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()

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()

Function to send the set of frequent patterns after completion of the mining process

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime()

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()

Frequent pattern mining process will start from here

printResults()

This function is used to print the results

save(outFile)

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

Parameters:

outFile (str) – name of the output file

startMine()

Frequent pattern mining process will start from here

PAMI.georeferencedFrequentPattern.basic.abstract module

Module contents