2. AI AND MACHINE LEARNING VTU LAB | READ NOW

MACHINE LEARNING VTU LAB

Program 2. FOR A GIVEN SET OF TRAINING DATA EXAMPLES STORED IN A .CSV FILE, IMPLEMENT AND DEMONSTRATE THE CANDIDATE-ELIMINATION ALGORITHM TO OUTPUT A DESCRIPTION OF THE SET
OF ALL HYPOTHESES CONSISTENT WITH THE TRAINING EXAMPLES

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Program Code – lab2.py

import numpy as np
import pandas as pd

# Loading Data from a CSV File
data = pd.DataFrame(data=pd.read_csv('trainingdata.csv'))
print(data)

# Separating concept features from Target
concepts = np.array(data.iloc[:,0:-1])
print(concepts)

# Isolating target into a separate DataFrame
# copying last column to target array
target = np.array(data.iloc[:,-1])
print(target)

def learn(concepts, target):
    
    '''
    learn() function implements the learning method of the Candidate elimination algorithm.
    Arguments:
        concepts - a data frame with all the features
        target - a data frame with corresponding output values
    '''

    # Initialise S0 with the first instance from concepts
    # .copy() makes sure a new list is created instead of just pointing to the same memory location
    specific_h = concepts[0].copy()
    print("\nInitialization of specific_h and general_h")
    print(specific_h)
    #h=["#" for i in range(0,5)]
    #print(h)

    general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]
    print(general_h)
    # The learning iterations
    for i, h in enumerate(concepts):

        # Checking if the hypothesis has a positive target
        if target[i] == "Yes":
            for x in range(len(specific_h)):

                # Change values in S & G only if values change
                if h[x] != specific_h[x]:
                    specific_h[x] = '?'
                    general_h[x][x] = '?'

        # Checking if the hypothesis has a positive target
        if target[i] == "No":
            for x in range(len(specific_h)):
                # For negative hyposthesis change values only  in G
                if h[x] != specific_h[x]:
                    general_h[x][x] = specific_h[x]
                else:
                    general_h[x][x] = '?'

        print("\nSteps of Candidate Elimination Algorithm",i+1)
        print(specific_h)
        print(general_h)
    
    # find indices where we have empty rows, meaning those that are unchanged
    indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]
    for i in indices:
        # remove those rows from general_h
        general_h.remove(['?', '?', '?', '?', '?', '?'])
    # Return final values
    return specific_h, general_h

s_final, g_final = learn(concepts, target)
print("\nFinal Specific_h:", s_final, sep="\n")
print("\nFinal General_h:", g_final, sep="\n")

MACHINE LEARNING Program Execution – lab2.ipynb

Jupyter Notebook program execution.

import numpy as np
import pandas as pd

# Loading Data from a CSV File
data = pd.DataFrame(data=pd.read_csv('trainingdata.csv'))
print(data)

sky airTemp humidity wind water forecast enjoySport

0 Sunny Warm Normal Strong Warm Same Yes

1 Sunny Warm High Strong Warm Same Yes

2 Rainy Cold High Strong Warm Change No

3 Sunny Warm High Strong Cool Change Yes

# Separating concept features from Target
concepts = np.array(data.iloc[:,0:-1])
print(concepts)

[[‘Sunny’ ‘Warm’ ‘Normal’ ‘Strong’ ‘Warm’ ‘Same’]

[‘Sunny’ ‘Warm’ ‘High’ ‘Strong’ ‘Warm’ ‘Same’]

[‘Rainy’ ‘Cold’ ‘High’ ‘Strong’ ‘Warm’ ‘Change’]

[‘Sunny’ ‘Warm’ ‘High’ ‘Strong’ ‘Cool’ ‘Change’]]

# Isolating target into a separate DataFrame
# copying last column to target array
target = np.array(data.iloc[:,-1])
print(target)

[‘Yes’ ‘Yes’ ‘No’ ‘Yes’]\

def learn(concepts, target):
    
    '''
    learn() function implements the learning method of the Candidate elimination algorithm.
    Arguments:
        concepts - a data frame with all the features
        target - a data frame with corresponding output values
    '''

    # Initialise S0 with the first instance from concepts
    # .copy() makes sure a new list is created instead of just pointing to the same memory location
    specific_h = concepts[0].copy()
    print("\nInitialization of specific_h and general_h")
    print(specific_h)
    #h=["#" for i in range(0,5)]
    #print(h)

    general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]
    print(general_h)
    # The learning iterations
    for i, h in enumerate(concepts):

        # Checking if the hypothesis has a positive target
        if target[i] == "Yes":
            for x in range(len(specific_h)):

                # Change values in S & G only if values change
                if h[x] != specific_h[x]:
                    specific_h[x] = '?'
                    general_h[x][x] = '?'

        # Checking if the hypothesis has a positive target
        if target[i] == "No":
            for x in range(len(specific_h)):
                # For negative hyposthesis change values only  in G
                if h[x] != specific_h[x]:
                    general_h[x][x] = specific_h[x]
                else:
                    general_h[x][x] = '?'

        print("\nSteps of Candidate Elimination Algorithm",i+1)
        print(specific_h)
        print(general_h)
    
    # find indices where we have empty rows, meaning those that are unchanged
    indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]
    for i in indices:
        # remove those rows from general_h
        general_h.remove(['?', '?', '?', '?', '?', '?'])
    # Return final values
    return specific_h, general_h

s_final, g_final = learn(concepts, target)
print("\nFinal Specific_h:", s_final, sep="\n")
print("\nFinal General_h:", g_final, sep="\n")

Initialization of specific_h and general_h

[‘Sunny’ ‘Warm’ ‘Normal’ ‘Strong’ ‘Warm’ ‘Same’]

[[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Steps of Candidate Elimination Algorithm 1

[‘Sunny’ ‘Warm’ ‘Normal’ ‘Strong’ ‘Warm’ ‘Same’]

[[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Steps of Candidate Elimination Algorithm 2

[‘Sunny’ ‘Warm’ ‘?’ ‘Strong’ ‘Warm’ ‘Same’]

[[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Steps of Candidate Elimination Algorithm 3

[‘Sunny’ ‘Warm’ ‘?’ ‘Strong’ ‘Warm’ ‘Same’]

[[‘Sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘Warm’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘Same’]]

Steps of Candidate Elimination Algorithm 4

[‘Sunny’ ‘Warm’ ‘?’ ‘Strong’ ‘?’ ‘?’]

[[‘Sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘Warm’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Final Specific_h:

[‘Sunny’ ‘Warm’ ‘?’ ‘Strong’ ‘?’ ‘?’]

Final General_h:

[[‘Sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘Warm’, ‘?’, ‘?’, ‘?’, ‘?’]]

Download the Program Dataset