COURSE LABORATORY MANUAL
1. EXPERIMENT NO: 7
2. TITLE: BAYESIAN NETWORK
3. LEARNING OBJECTIVES:
• Make use of Data sets in implementing the machine learning algorithms.
• Implement ML concepts and algorithms in Python
4. AIM:
• Write a program to construct a Bayesian network considering medical data. Use this model
to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You
can use Java/Python ML library classes/API.
5. THEORY:
• Bayesian networks are very convenient for representing similar probabilistic relationships
between multiple events.
• Bayesian networks as graphs - People usually represent Bayesian
networks as directed graphs in which each node is a hypothesis or a
random process. In other words, something that takes at least 2
possible values you can assign probabilities to. For example, there can
be a node that represents the state of the dog (barking or not barking at
the window), the weather (raining or not raining), etc.
• The arrows between nodes represent the conditional probabilities
between them — how information about the state of one node changes
the probability distribution of another node it’s connected to.
6. PROCEDURE / PROGRAMME :
Program for the Illustration of Baysian Belief networks using 5 nodes using Lung cancer data. (The
Conditional probabilities are given)
from pgmpy.models import BayesianModel from
pgmpy.factors.discrete import TabularCPD
from pgmpy.inference import VariableElimination
#Define a Structure with nodes and edge cancer_model =
BayesianModel([('Pollution', 'Cancer'),
('Smoker', 'Cancer'),
('Cancer', 'Xray'),
('Cancer', 'Dyspnoea')])
print('Baysian network nodes are:')
print('\t',cancer_model.nodes())
print('Baysian network edges are:')
print('\t',cancer_model.edges())
#Creation of Conditional Probability Table
cpd_poll = TabularCPD(variable='Pollution', variable_card=2,
values=[[0.9], [0.1]])
cpd_smoke= TabularCPD(variable='Smoker', variable_card=2,
values=[[0.3], [0.7]])
cpd_cancer= TabularCPD(variable='Cancer', variable_card=2,
COURSE LABORATORY MANUAL
values=[[0.03, 0.05, 0.001, 0.02],
[0.97, 0.95, 0.999, 0.98]],
evidence=['Smoker', 'Pollution'],
evidence_card=[2, 2])
cpd_xray = TabularCPD(variable='Xray', variable_card=2, values=[[0.9,
0.2], [0.1, 0.8]],
evidence=['Cancer'], evidence_card=[2]) cpd_dysp =
TabularCPD(variable='Dyspnoea', variable_card=2,
values=[[0.65, 0.3], [0.35, 0.7]],
evidence=['Cancer'], evidence_card=[2])
# Associating the parameters with the model structure. cancer_model.add_cpds(cpd_poll,
cpd_smoke, cpd_cancer, cpd_xray, cpd_dysp) print('Model generated by adding
conditional probability disttributions(cpds)')
# Checking if the cpds are valid for the model. print('Checking
for Correctness of model : ', end='' )
print(cancer_model.check_model())
'''print('All local idependencies are as follows')
cancer_model.get_independencies()
'''
print('Displaying CPDs')
print(cancer_model.get_cpds('Pollution'))
print(cancer_model.get_cpds('Smoker'))
print(cancer_model.get_cpds('Cancer'))
print(cancer_model.get_cpds('Xray'))
print(cancer_model.get_cpds('Dyspnoea'))
##Inferencing with Bayesian Network
# Computing the probability of Cancer given smoke.
cancer_infer = VariableElimination(cancer_model)
print('\nInferencing with Bayesian Network'); print('\nProbability
of Cancer given Smoker')
q = cancer_infer.query(variables=['Cancer'], evidence={'Smoker': 1})
print(q['Cancer'])
print('\nProbability of Cancer given Smoker,Pollution')
q = cancer_infer.query(variables=['Cancer'], evidence={'Smoker': 1,'Pollution': 1}) print(q['Cancer'])
Program as per the Syllubus
import numpy as np
import pandas as pd
import csv
from pgmpy.estimators import MaximumLikelihoodEstimator from
pgmpy.models import BayesianModel
from pgmpy.inference import VariableElimination
#Read the attributes
lines = list(csv.reader(open('data7_names.csv', 'r'))); attributes
= lines[0]
#Read Cleveland Heart dicease data
heartDisease = pd.read_csv('data7_heart.csv', names = attributes) heartDisease =
heartDisease.replace('?', np.nan)
COURSE LABORATORY MANUAL
# Display the data
#print('Few examples from the dataset are given below')
#print(heartDisease.head())
#print('\nAttributes and datatypes')
#print(heartDisease.dtypes)
# Model Baysian Network
model = BayesianModel([('age', 'trestbps'), ('age', 'fbs'), ('sex', 'trestbps'), ('sex', 'trestbps'),
('exang', 'trestbps'),('trestbps','heartdisease'),('fbs','heartdisease'),
('heartdisease','restecg'),('heartdisease','thalach'),('heartdisease','chol')])
# Learning CPDs using Maximum Likelihood Estimators print('\nLearning CPDs
using Maximum Likelihood Estimators...'); model.fit(heartDisease,
estimator=MaximumLikelihoodEstimator)
# Inferencing with Bayesian Network print('\nInferencing with
Bayesian Network:') HeartDisease_infer =
VariableElimination(model)
# Computing the probability of bronc given smoke.
print('\n1.Probability of HeartDisease given Age=20')
q = HeartDisease_infer.query(variables=['heartdisease'], evidence={'age': 28}) print(q['heartdisease'])
print('\n2. Probability of HeartDisease given chol (Cholestoral) =100')
q = HeartDisease_infer.query(variables=['heartdisease'], evidence={'chol': 100}) print(q['heartdisease'])
7. RESULTS & CONCLUSIONS:
Dataset
data7_names.csv (14 attributes) age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,
slope,ca,thal,heartdisease
data7_heart.csv (5 instances out of 303)
63.0,1.0,1.0,145.0,233.0,1.0,2.0,150.0,0.0,2.3,3.0,0.0,6.0,0
67.0,1.0,4.0,160.0,286.0,0.0,2.0,108.0,1.0,1.5,2.0,3.0,3.0,2
67.0,1.0,4.0,120.0,229.0,0.0,2.0,129.0,1.0,2.6,2.0,2.0,7.0,1
37.0,1.0,3.0,130.0,250.0,0.0,0.0,187.0,0.0,3.5,3.0,0.0,3.0,0
41.0,0.0,2.0,130.0,204.0,0.0,2.0,172.0,0.0,1.4,1.0,0.0,3.0,0
Output
Learing CPDs using Maximum Likelihood Estimators... Inferencing
with Bayesian Network:
1.Probability of HeartDisease given Age=20
╒════════════════╤═════════════════════╕
│ heartdisease │ phi(heartdisease) │
╞════════════════╪═════════════════════╡
│ heartdisease_0 │ 0.6791 │
├────────────────┼─────────────────────┤
│ heartdisease_1 │ 0.1212 │
├────────────────┼─────────────────────┤
│ heartdisease_2 │ 0.0810 │
├────────────────┼─────────────────────┤
│ heartdisease_3 │ 0.0939 │
├────────────────┼─────────────────────┤
│ heartdisease_4 │ 0.0247 │
╘════════════════╧═════════════════════╛
COURSE LABORATORY MANUAL
2. Probability of HeartDisease given chol (Cholestoral) =100
╒════════════════╤═════════════════════╕
│ heartdisease │ phi(heartdisease) │
╞════════════════╪═════════════════════╡
│ heartdisease_0 │ 0.5400 │
├────────────────┼─────────────────────┤
│ heartdisease_1 │ 0.1533 │
├────────────────┼─────────────────────┤
│ heartdisease_2 │ 0.1303 │
├────────────────┼─────────────────────┤
│ heartdisease_3 │ 0.1259 │
├────────────────┼─────────────────────┤
│ heartdisease_4 │ 0.0506 │
╘════════════════╧═════════════════════╛
8. LEARNING OUTCOMES :
• The student will be able to apply baysian network for the medical data and demonstrate the
diagnosis of heart patients using standard Heart Disease Data Set.
9. APPLICATION AREAS:
• Applicable in prediction and classification • Document Classification
• Gene Regulatory Networks • Information Retrieval
• Medicine • Semantic Search
• Biomonitoring
10. REMARKS:
