This page first describes the syntax to implement a mining algorithm on a database at different constraint values. Next, this page provides an example to execute a pattern mining algorithm on a database at different constraint values.

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Syntax to implement an algorithm

# import the necessary algorithm from the PAMI library
from PAMI.<model>.<basic/closed/maximal/topK> import <algorithmName> as alg
algorithm='Algorithm Name'

#Create a data frame to store the values
import pandas as pd
result = pd.DataFrame(columns=['algorithm', 'minSup', 'patterns', 'runtime', 'memory'])

#Specify the List of constraint values
constraintList =['List of values']

#For each value in the constraint list
for constraint in constraintList:
    # Call the necessary algorithm by passing necessary input parameters. The input parameters include inputFileName and the user-specified constraints.
    obj = alg.<algorithmName>(<input parameters>)

    # Start the mining algorithm
    obj.startMine()

    #store the results in the data frame
    result.loc[result.shape[0]] = [algorithm, constraint, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]

#Print the dataframe
result

#Visualize the plots
from PAMI.extras.graph import dataFrameInToFigures as dif
    #Pass the result data frame to the class
    ab = dif.dataFrameInToFigures(result)
    #Draw the graphs
    ab.plotGraphsFromDataFrame()

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Example: Mining frequent patterns in a transactional database at different mininimum support values using FP-growth

#Import the mining algorithm
from PAMI.frequentPattern.basic import FPGrowth  as alg

#Import pandas data frame to store the values
import pandas as pd

#Initialize the data frame
result = pd.DataFrame(columns=['algorithm', 'minSup', 'patterns', 'runtime', 'memory'])


#Specify the name of the input file
inputFile = 'Transactional_T10I4D100K.csv'

#specify the seperator
seperator='\t'

#Create a list of constraint values
minimumSupportList = [500,600,700,800]
#minimumSupport values can be specified between 0 to 1.
#Example: minSupList = [0.005, 0.006, 0.007, 0.008, 0.009]

#specify the name of the algorithm
algorithm = 'FP-growth'  #specify the algorithm name

#Run the for loop for each minSup value
for minSup in minimumSupportList:
    #Create the object
    obj = alg.FPGrowth(inputFile, minSup, seperator)
    #start the mining process
    obj.startMine()
    #store the results in the data frame
    result.loc[result.shape[0]] = [algorithm, minSup, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]

#Print the dataframe
result

#Visualize the plots
from PAMI.extras.graph import dataFrameInToFigures as dif
#Pass the result data frame to the class
ab = dif.dataFrameInToFigures(result)
#Draw the graphs
ab.plotGraphsFromDataFrame()