Candidate Elimination Algorithm in Python

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Python Program to Implement Candidate Elimination Algorithm to get Consistent Version Space

Exp. No. 2. For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm in python to output a description of the set of all hypotheses consistent with the training examples.

Candidate Elimination Algorithm Machine Learning

For each training example d, do:
     If d is positive example
         Remove from G any hypothesis h inconsistent with d
         For each hypothesis s in S not consistent with d:
              Remove s from S
              Add to S all minimal generalizations of s consistent with d and having a generalization in G
              Remove from S any hypothesis with a more specific h in S
     If d is negative example
          Remove from S any hypothesis h inconsistent with d
          For each hypothesis g in G not consistent with d:
                Remove g from G
                Add to G all minimal specializations of g consistent with d and having a specialization in S
                Remove from G any hypothesis having a more general hypothesis in G

Python Program to Implement and Demonstrate FIND-S Algorithm

import numpy as np 
import pandas as pd

data = pd.read_csv(path+'/enjoysport.csv')
concepts = np.array(data.iloc[:,0:-1])
print("\nInstances are:\n",concepts)
target = np.array(data.iloc[:,-1])
print("\nTarget Values are: ",target)

def learn(concepts, target): 
    specific_h = concepts[0].copy()
    print("\nInitialization of specific_h and genearal_h")
    print("\nSpecific Boundary: ", specific_h)
    general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]
    print("\nGeneric Boundary: ",general_h)  

    for i, h in enumerate(concepts):
        print("\nInstance", i+1 , "is ", h)
        if target[i] == "yes":
            print("Instance is Positive ")
            for x in range(len(specific_h)): 
                if h[x]!= specific_h[x]:                    
                    specific_h[x] ='?'                     
                    general_h[x][x] ='?'
                   
        if target[i] == "no":            
            print("Instance is Negative ")
            for x in range(len(specific_h)): 
                if h[x]!= specific_h[x]:                    
                    general_h[x][x] = specific_h[x]                
                else:                    
                    general_h[x][x] = '?'        
        
        print("Specific Bundary after ", i+1, "Instance is ", specific_h)         
        print("Generic Boundary after ", i+1, "Instance is ", general_h)
        print("\n")

    indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]    
    for i in indices:   
        general_h.remove(['?', '?', '?', '?', '?', '?']) 
    return specific_h, general_h 

s_final, g_final = learn(concepts, target)

print("Final Specific_h: ", s_final, sep="\n")
print("Final General_h: ", g_final, sep="\n")

Dataset:

EnjoySport Dataset is saved as .csv (comma separated values) file in the current working directory otherwise use the complete path of the dataset set in the program:

Output:

Instances are:
[[‘sunny’ ‘warm’ ‘normal’ ‘strong’ ‘warm’ ‘same’]
[‘sunny’ ‘warm’ ‘high’ ‘strong’ ‘warm’ ‘same’]
[‘rainy’ ‘cold’ ‘high’ ‘strong’ ‘warm’ ‘change’]
[‘sunny’ ‘warm’ ‘high’ ‘strong’ ‘cool’ ‘change’]]

Target Values are: [‘yes’ ‘yes’ ‘no’ ‘yes’]

Initialization of specific_h and genearal_h

Specific Boundary: [‘sunny’ ‘warm’ ‘normal’ ‘strong’ ‘warm’ ‘same’]

Generic Boundary: [[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Instance 1 is [‘sunny’ ‘warm’ ‘normal’ ‘strong’ ‘warm’ ‘same’] Instance is Positive

Specific Bundary after 1 Instance is [‘sunny’ ‘warm’ ‘normal’ ‘strong’ ‘warm’ ‘same’]

Generic Boundary after 1 Instance is [[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Instance 2 is [‘sunny’ ‘warm’ ‘high’ ‘strong’ ‘warm’ ‘same’] Instance is Positive

Specific Bundary after 2 Instance is [‘sunny’ ‘warm’ ‘?’ ‘strong’ ‘warm’ ‘same’]

Generic Boundary after 2 Instance is [[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Instance 3 is [‘rainy’ ‘cold’ ‘high’ ‘strong’ ‘warm’ ‘change’] Instance is Negative

Specific Bundary after 3 Instance is [‘sunny’ ‘warm’ ‘?’ ‘strong’ ‘warm’ ‘same’]

Generic Boundary after 3 Instance is [[‘sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘warm’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘same’]]

Instance 4 is [‘sunny’ ‘warm’ ‘high’ ‘strong’ ‘cool’ ‘change’] Instance is Positive

Specific Bundary after 4 Instance is [‘sunny’ ‘warm’ ‘?’ ‘strong’ ‘?’ ‘?’]

Generic Boundary after 4 Instance is [[‘sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘warm’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Final Specific_h: [‘sunny’ ‘warm’ ‘?’ ‘strong’ ‘?’ ‘?’]

Final General_h: [[‘sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘warm’, ‘?’, ‘?’, ‘?’, ‘?’]]

Solved Numerical Examples:

Candidate Elimination Algorithm Solved Example – 1

Candidate Elimination Algorithm Solved Example – 2

Candidate Elimination Algorithm Solved Example – 3

Summary

This tutorial discusses how to Implement and demonstrate the Candidate Elimination algorithm in Python for finding the Consistent version space based on a given set of training data samples. The training data is read from a .CSV file. If you like the tutorial share with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.