PAMI.frequentPattern.basic package

Submodules

PAMI.frequentPattern.basic.Apriori module

class PAMI.frequentPattern.basic.Apriori.Apriori(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

Apriori is one of the fundamental algorithm to discover frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively. This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a transactional database.

Reference:

Agrawal, R., Imieli ́nski, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD. pp. 207–216 (1993), https://doi.org/10.1145/170035.170072

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

• 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.

• 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

Databaselist

To store the transactions of a database in list

Methods to execute code on terminal

Format:

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

Example Usage:

(.venv) $ python3 Apriori.py sampleDB.txt patterns.txt 10.0

Note

minSup will be considered in percentage of database transactions

Importing this algorithm into a python program

import PAMI.frequentPattern.basic.Apriori as alg

obj = alg.Apriori(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

print("Total number of Frequent Patterns:", len(frequentPatterns))

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.

bitPacker(data, maxIndex)

getMemoryRSS() → float

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() → float

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() → Dict[str, int]

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float

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() → None

Frequent pattern mining process will start from here

printResults() → None

This function is used to print the result

save(outFile) → None

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

Parameters:

outFile (csvfile) – name of the output file

Returns:

None

startMine() → None

Frequent pattern mining process will start from here

PAMI.frequentPattern.basic.AprioriOLD module

class PAMI.frequentPattern.basic.AprioriOLD.Apriori(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

Apriori is one of the fundamental algorithm to discover frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to reduce the search space effectively. This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a transactional database.

Reference:

Agrawal, R., Imieli ́nski, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD. pp. 207–216 (1993), https://doi.org/10.1145/170035.170072

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

• 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.

• 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

Databaselist

To store the transactions of a database in list

Methods to execute code on terminal

Format:

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

Example Usage:

(.venv) $ python3 Apriori.py sampleDB.txt patterns.txt 10.0

Note

minSup will be considered in percentage of database transactions

Importing this algorithm into a python program

import PAMI.frequentPattern.basic.Apriori as alg

obj = alg.Apriori(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

print("Total number of Frequent Patterns:", len(frequentPatterns))

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() → float

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() → float

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() → Dict[str, int]

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float

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() → None

Frequent pattern mining process will start from here

printResults() → None

This function is used to print the result

save(outFile) → None

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

Parameters:

outFile (csvfile) – name of the output file

Returns:

None

startMine() → None

Frequent pattern mining process will start from here

PAMI.frequentPattern.basic.ECLAT module

class PAMI.frequentPattern.basic.ECLAT.ECLAT(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

ECLAT is one of the fundamental algorithm to discover frequent patterns in a transactional database.

Reference:

Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): 372-390 (2000), https://ieeexplore.ieee.org/document/846291

Parameters:

• 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

• 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.

• 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

Databaselist

To store the transactions of a database in list

Methods to execute code on terminal

Format:

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

Example Usage:

(.venv) $ python3 ECLAT.py sampleDB.txt patterns.txt 10.0

Note

minSup will be considered in percentage of database transactions

Importing this algorithm into a python program

import PAMI.frequentPattern.basic.ECLAT as alg

obj = alg.ECLAT(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

print("Total number of Frequent Patterns:", len(frequentPatterns))

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 Kundai under the supervision of Professor Rage Uday Kiran.

getMemoryRSS() → float

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() → float

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() → dict

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float

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() → None

Frequent pattern mining process will start from here

printResults() → None

Function used to print the results

save(outFile: str) → None

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

Parameters:

outFile (csvfile) – name of the output file

Returns:

None

startMine() → None

Frequent pattern mining process will start from here

PAMI.frequentPattern.basic.ECLATDiffset module

class PAMI.frequentPattern.basic.ECLATDiffset.ECLATDiffset(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

ECLATDiffset uses diffset to extract the frequent patterns in a transactional database.

Reference:

KDD ‘03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining August 2003 Pages 326–335 https://doi.org/10.1145/956750.956788

Parameters:

• 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

• 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.

• 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

Databaselist

To store the transactions of a database in list

Methods to execute code on terminal

Format:

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

Example Usage:

(.venv) $ python3 ECLATDiffset.py sampleDB.txt patterns.txt 10.0

Note

minSup will be considered in percentage of database transactions

Importing this algorithm into a python program

import PAMI.frequentPattern.basic.ECLATDiffset as alg

obj = alg.ECLATDiffset(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

print("Total number of Frequent Patterns:", len(frequentPatterns))

obj.savePatterns(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 Kundai 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 :return: returning RSS memory consumed by the mining process :rtype: float

getMemoryUSS()

Total amount of USS memory consumed by the mining process will be retrieved from this function :return: returning USS memory consumed by the mining process :rtype: float

getPatterns()

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame

getRuntime()

Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: 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 an output file :param outFile: name of the output file :type outFile: csvfile

startMine()

Frequent pattern mining process will start from here

PAMI.frequentPattern.basic.ECLATbitset module

class PAMI.frequentPattern.basic.ECLATbitset.ECLATbitset(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

ECLATbitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.

Reference:

Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): 372-390 (2000), https://ieeexplore.ieee.org/document/846291

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

• 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.

• 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

Databaselist

To store the transactions of a database in list

Methods to execute code on terminal

Format:

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

Example Usage:

(.venv) $ python3 ECLATbitset.py sampleDB.txt patterns.txt 10.0

Note

minSup will be considered in percentage of database transactions

Importing this algorithm into a python program

import PAMI.frequentPattern.basic.ECLATbitset as alg

obj = alg.ECLATbitset(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

print("Total number of Frequent Patterns:", len(frequentPatterns))

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 Yudai Masu under the supervision of Professor Rage Uday Kiran.

creatingFrequentItems()

This function creates frequent items from _database.

Returns:

frequentTidData that stores frequent items and their tid list.

Return type:

Dict

genAllFrequentPatterns(frequentItems)

This function generates all frequent patterns.

Parameters:

frequentItems (Dict) – frequent items

genPatterns(prefix, tidData)

This function generate frequent pattern about prefix.

Parameters:

• prefix (str) – prefix of pattern to generate patterns

• tidData (list) – tidData for pattern generation

getMemoryRSS()

Total amount of RSS memory consumed by the mining process will be retrieved from this function :return: returning RSS memory consumed by the mining process :rtype: float

getMemoryUSS()

Total amount of USS memory consumed by the mining process will be retrieved from this function :return: returning USS memory consumed by the mining process :rtype: float

getPatterns()

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame()

Storing final frequent patterns in a dataframe :return: returning frequent patterns in a dataframe :rtype: pd.DataFrame

getRuntime()

Calculating the total amount of runtime taken by the mining process :return: returning total amount of runtime taken by the mining process :rtype: float

mine()

Frequent pattern mining process will start from here We start with the scanning the itemSets and store the bitsets respectively. We form the combinations of single items and check with minSup condition to check the frequency of patterns

printResults()

This function is used to print the result

save(outFile)

Complete set of frequent patterns will be loaded in to an output file :param outFile: name of the outputfile :type outFile: file

startMine()

Frequent pattern mining process will start from here We start with the scanning the itemSets and store the bitsets respectively. We form the combinations of single items and check with minSup condition to check the frequency of patterns

tidToBitset(itemset)

This function converts tid list to bitset.

Parameters:

itemset (Dict) – frequent itemset that generated

Returns:

patterns with original item names

Return type:

Dict

PAMI.frequentPattern.basic.FPGrowth module

class PAMI.frequentPattern.basic.FPGrowth.FPGrowth(iFile, minSup, sep='\t')

Bases: _frequentPatterns

Description:

FPGrowth is one of the fundamental algorithm to discover frequent patterns in a transactional database. It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs downward closure property to reduce the search space effectively.

Reference:

Han, J., Pei, J., Yin, Y. et al. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery 8, 53–87 (2004). https://doi.org/10.1023

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

• 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.

• 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

Databaselist

To store the transactions of a database in list

mapSupportDictionary

To maintain the information of item and their frequency

lnoint

it represents the total no of transactions

treeclass

it represents the Tree class

finalPatternsdict

it represents to store the patterns

Methods to execute code on terminal

Format:

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

Example Usage:

(.venv) $ python3 FPGrowth.py sampleDB.txt patterns.txt 10.0

Note

minSup will be considered in percentage of database transactions

Importing this algorithm into a python program

from PAMI.frequentPattern.basic import FPGrowth as alg

obj = alg.FPGrowth(iFile, minSup)

obj.mine()

frequentPatterns = obj.getPatterns()

print("Total number of Frequent Patterns:", len(frequentPatterns))

obj.savePatterns(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() → float

Total amount of RSS memory consumed by the mining process will be retrieved from this function :return: returning RSS memory consumed by the mining process :rtype: float

getMemoryUSS() → float

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() → Dict[str, int]

Function to send the set of frequent patterns after completion of the mining process :return: returning frequent patterns :rtype: dict

getPatternsAsDataFrame() → DataFrame

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float

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() → None

Main program to start the operation

printResults() → None

This function is used to print the results

save(outFile: str) → None

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

Parameters:

outFile (csvfile) – name of the output file

Returns:

None

startMine() → None

Main program to start the operation

PAMI.frequentPattern.basic.abstract module

Module contents