PAMI.frequentPattern.basic package

Submodules

PAMI.frequentPattern.basic.Apriori module

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

Bases: _frequentPatterns

About this algorithm

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 or URL or dataFrame) – 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:

  • 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.

  • memoryUSS (float) – To store the total amount of USS memory consumed by the program.

  • memoryRSS (float) – To store the total amount of RSS memory consumed by the program.

  • Database (list) – To store the transactions of a database in list.

Execution methods

Terminal command

Format:

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

Example Usage:

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

Note

minSup can be specified in support count or a value between 0 and 1.

Calling from a python program

import PAMI.frequentPattern.basic.Apriori as alg

iFile = 'sampleDB.txt'

minSup = 10  # can also be specified between 0 and 1

obj = alg.Apriori(iFile, minSup)

obj.mine()

frequentPattern = obj.getPatterns()

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

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 and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

getMemoryRSS() → float[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() → float[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() → Dict[str, int][source]

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame[source]

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float[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(memorySaver=True) → None[source]

Frequent pattern mining process will start from here

memorySaver

This attribute is used to enable or disable memory saving mode. By default, it is enabled. It saves the memory by deleting the intermediate results after the completion of the mining process.

Type:

bool

printResults() → None[source]

This function is used to print the result

save(oFile: str, seperator='\t') → None[source]

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

Parameters:

  • oFile (csvfile) – name of the output file

  • seperator (string) – variable to store separator value

Returns:

None

startMine() → None[source]

Frequent pattern mining process will start from here

PAMI.frequentPattern.basic.ECLAT module

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

Bases: _frequentPatterns

About this algorithm

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 or URL or dataFrame) – 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:

  • 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.

  • memoryUSS (float) – To store the total amount of USS memory consumed by the program.

  • memoryRSS (float) – To store the total amount of RSS memory consumed by the program.

  • Database (list) – To store the transactions of a database in list.

Execution methods

Terminal command

Format:

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

Example Usage:

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

Note

minSup can be specified in support count or a value between 0 and 1.

Calling from a python program

import PAMI.frequentPattern.basic.ECLAT as alg

iFile = 'sampleDB.txt'

minSup = 10  # can also be specified between 0 and 1

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 and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

getMemoryRSS() → float[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() → float[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() → dict[source]

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame[source]

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float[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(memorySaver=True) → None[source]

Frequent pattern mining process will start from here

printResults() → None[source]

Function used to print the results

save(outFile: str, seperator='\t') → None[source]

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

Parameters:

  • outFile (csvfile) – name of the output file

  • seperator (string) – variable to store separator value

Returns:

None

startMine() → None[source]

Frequent pattern mining process will start from here

PAMI.frequentPattern.basic.ECLATDiffset module

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

Bases: _frequentPatterns

About this algorithm

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 or URL or dataFrame) – 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:

  • startTime (float) – To record the start time of the mining process.

  • endTime (float) – To record the end time of the mining process.

  • finalPatterns (dict) – Storing the complete set of patterns in a dictionary variable.

  • memoryUSS (float) – To store the total amount of USS memory consumed by the program.

  • memoryRSS (float) – To store the total amount of RSS memory consumed by the program.

  • Database (list) – To store the transactions of a database in list.

Execution methods

Terminal command

Format:

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

Example Usage:

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

Note

minSup can be specified in support count or a value between 0 and 1.

Calling from a python program

import PAMI.frequentPattern.basic.ECLATDiffset as alg

iFile = 'sampleDB.txt'

minSup = 10  # can also be specified between 0 and 1

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 and revised by Tarun Sreepada 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]

This function returns the 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: str, seperator='\t') → None[source]

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

Parameters:

  • outFile (csvfile) – name of the output file

  • seperator (string) – variable to store separator value

Returns:

None

startMine()[source]

Frequent pattern mining process will start from here

PAMI.frequentPattern.basic.ECLATbitset module

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

Bases: _frequentPatterns

About this algorithm

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 or URL or dataFrame) – 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:

  • startTime (float) – To record the start time of the mining process.

  • endTime (float) – To record the end time of the mining process.

  • finalPatterns (dict) – Storing the complete set of patterns in a dictionary variable.

  • memoryUSS (float) – To store the total amount of USS memory consumed by the program.

  • memoryRSS (float) – To store the total amount of RSS memory consumed by the program.

  • Database (list) – To store the transactions of a database in list.

Execution methods

Terminal command

Format:

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

Example Usage:

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

Note

minSup can be specified in support count or a value between 0 and 1.

Calling from a python program

import PAMI.frequentPattern.basic.ECLATbitset as alg

iFile = 'sampleDB.txt'

minSup = 10  # can also be specified between 0 and 1

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 and revised by Tarun Sreepada 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(memorySaver=True) → None[source]

Frequent pattern mining process will start from here # Bitset implementation

printResults()[source]

This function is used to print the result

save(outFile: str, seperator='\t') → None[source]

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

Parameters:

  • outFile (csvfile) – name of the output file

  • seperator (string) – variable to store separator value

Returns:

None

startMine()[source]

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

PAMI.frequentPattern.basic.FPGrowth module

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

Bases: _frequentPatterns

About this algorithm

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 or URL or dataFrame) – 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:

  • 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.

  • memoryUSS (float) – To store the total amount of USS memory consumed by the program.

  • memoryRSS (float) – To store the total amount of RSS memory consumed by the program.

  • Database (list) – To store the transactions of a database in list.

  • mapSupport (Dictionary) – To maintain the information of item and their frequency.

  • tree (class) – it represents the Tree class.

Execution methods

Terminal command

Format:

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

Example Usage:

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

Note

minSup can be specified in support count or a value between 0 and 1.

Calling from a python program

from PAMI.frequentPattern.basic import FPGrowth as alg

iFile = 'sampleDB.txt'

minSup = 10  # can also be specified between 0 and 1

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 and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.

getMemoryRSS() → float[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() → float[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() → Dict[str, int][source]

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

Returns:

returning frequent patterns

Return type:

dict

getPatternsAsDataFrame() → DataFrame[source]

Storing final frequent patterns in a dataframe

Returns:

returning frequent patterns in a dataframe

Return type:

pd.DataFrame

getRuntime() → float[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() → None[source]

Main program to start the operation

printResults() → None[source]

This function is used to print the results

save(outFile: str, seperator='\t') → None[source]

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

Parameters:

  • outFile (csvfile) – name of the output file

  • seperator (string) – variable to store the separator

Returns:

None

startMine()[source]

Starting the mining process

PAMI.frequentPattern.basic.abstract module

Module contents