Name : Sri Varshan P

Register No. 212222240104

Experiment 1

DATE:

Implementation of Bayesian Networks

Aim :

To create a bayesian Network for the given dataset in Python

Algorithm:

Step 1:

Import necessary libraries: pandas, networkx, matplotlib.pyplot, Bbn, Edge, EdgeType, BbnNode, Variable, EvidenceBuilder, InferenceController

Step 2:

Set pandas options to display more columns

Step 3:

Read in weather data from a CSV file using pandas

Step 4:

Remove records where the target variable RainTomorrow has missing values

Step 5:

Fill in missing values in other columns with the column mean

Step 6:

Create bands for variables that will be used in the model (Humidity9amCat, Humidity3pmCat, and WindGustSpeedCat)

Step 7:

Define a function to calculate probability distributions, which go into the Bayesian Belief Network (BBN)

Step 8:

Create BbnNode objects for Humidity9amCat, Humidity3pmCat, WindGustSpeedCat, and RainTomorrow, using the probs() function to calculate their probabilities

Step 9:

Create a Bbn object and add the BbnNode objects to it, along with edges between the nodes

Step 10:

Convert the BBN to a join tree using the InferenceController

Step 11:

Set node positions for the graph

Step 12:

Set options for the graph appearance

Step 13:

Generate the graph using networkx

Step 14:

Update margins and display the graph using matplotlib.pyplot

Program:

!pip install pybbn

import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
from pybbn.graph.dag import Bbn
from pybbn.graph.dag import Edge,EdgeType
from pybbn.graph.jointree import EvidenceBuilder
from pybbn.graph.node import BbnNode
from pybbn.graph.variable import Variable
from pybbn.pptc.inferencecontroller import InferenceController
pd.options.display.max_columns=50

df = pd.read_csv('weatherAUS.csv', encoding='utf-8')

df = df[pd.isnull(df['RainTomorrow']) == False]

df = df.fillna(df.mean())

df['WindGustSpeedCat'] = df['WindGustSpeed'].apply(lambda x: '0.<=40' if x <= 40 else '1.40-50' if 40 < x <= 50 else '2.>50')
df['Humidity9amCat'] = df['Humidity9am'].apply(lambda x: '1.>60' if x > 60 else '0.<=60')
df['Humidity3pmCat'] = df['Humidity3pm'].apply(lambda x: '1.>60' if x > 60 else '0.<=60')

def probs(data, child, parent1=None, parent2=None):
    if parent1 == None:

        prob = pd.crosstab(data[child], 'Empty', margins=False, normalize='columns').sort_index().to_numpy().reshape(-1).tolist()
    elif parent1!= None:

        if parent2 == None:
            \
            prob = pd.crosstab(data[parent1], data[child], margins=False, normalize='index').sort_index().to_numpy().reshape(-1).tolist()
        else:

            prob = pd.crosstab([data[parent1], data[parent2]], data[child], margins=False, normalize='index').sort_index().to_numpy().reshape(-1).tolist()
    else:
        print("Error in Probability Frequency Calculations")
    return prob

H9am = BbnNode(Variable(0, "H9am", ['<=60', '>60']), probs(df, child="Humidity9amCat"))
H3pm = BbnNode(Variable(1, "H3pm", ['<=60', '>60']), probs(df, child="Humidity3pmCat", parent1="Humidity9amCat"))
W = BbnNode(Variable(2, "W", ['<=40', '40-50', '>50']), probs(df, child="WindGustSpeedCat"))
RT = BbnNode(Variable(3, "RT", ['No', 'Yes']), probs(df, child="RainTomorrow", parent1="Humidity3pmCat", parent2="WindGustSpeedCat"))

bbn = Bbn() \
    .add_node(H9am) \
    .add_node(H3pm) \
    .add_node(W) \
    .add_node(RT) \
    .add_edge(Edge(H9am, H3pm, EdgeType.DIRECTED)) \
    .add_edge(Edge(H3pm, RT, EdgeType.DIRECTED)) \
    .add_edge(Edge(W, RT, EdgeType.DIRECTED))

join_tree = InferenceController.apply(bbn)


pos = {0: (-1,2), 1: (-1,0.5), 2: (1,0.5), 3: (0,-1)}

options = {
    "font_size" : 16,
    "node_size" : 4000,
    "node_color" : "Yellow",
    "edgecolors" : "Red",
    "edge_color" : "Black",
    "linewidths" : 5,
    "width" : 5,
}

n , d = bbn.to_nx_graph()
nx.draw(n, with_labels = True,labels = d,pos = pos , **options)

ax = plt.gca()
ax.margins(0.10)
plt.axis("off")
plt.show()

Output:

Result:

Thus a Bayesian Network is generated using Python

psrivarshan / ex1-aai Goto Github PK

ex1-aai's Introduction

Name : Sri Varshan P

Register No. 212222240104

Experiment 1

DATE:

Implementation of Bayesian Networks

Aim :

To create a bayesian Network for the given dataset in Python

Algorithm:

Step 1:

Import necessary libraries: pandas, networkx, matplotlib.pyplot, Bbn, Edge, EdgeType, BbnNode, Variable, EvidenceBuilder, InferenceController

Step 2:

Set pandas options to display more columns

Step 3:

Read in weather data from a CSV file using pandas

Step 4:

Remove records where the target variable RainTomorrow has missing values

Step 5:

Fill in missing values in other columns with the column mean

Step 6:

Create bands for variables that will be used in the model (Humidity9amCat, Humidity3pmCat, and WindGustSpeedCat)

Step 7:

Define a function to calculate probability distributions, which go into the Bayesian Belief Network (BBN)

Step 8:

Create BbnNode objects for Humidity9amCat, Humidity3pmCat, WindGustSpeedCat, and RainTomorrow, using the probs() function to calculate their probabilities

Step 9:

Create a Bbn object and add the BbnNode objects to it, along with edges between the nodes

Step 10:

Convert the BBN to a join tree using the InferenceController

Step 11:

Set node positions for the graph

Step 12:

Set options for the graph appearance

Step 13:

Generate the graph using networkx

Step 14:

Update margins and display the graph using matplotlib.pyplot

Program:

Output:

Result:

Thus a Bayesian Network is generated using Python

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