Advance your career in a data-driven industry by utilizing graphical AI-modeling techniques in Python to construct and optimize causal Bayesian networks.

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This course introduces you to Bayesian networks, an inductive reasoning approach ideal for situations with limited data but access to expert knowledge. Whether you’re a developer, data scientist, or AI enthusiast, mastering Bayesian networks in Python is essential to your problem-solving toolkit.

You’ll start with the fundamentals of Bayesian networks in Python to establish network criteria and interpret data. You’ll then create and optimize network structures and explore how structured information or simulated data can be transformed into actionable Bayesian networks. Next, you’ll master hyperparameter tuning, query analysis, and the best heuristic to construct Bayesian networks.

By the end of this course, you’ll have the tools to refine and apply your new skills in real-world modeling contexts. You’ll be proficient in evaluating Bayesian networks using various metrics, including ROC curve analysis, to design and interpret powerful models, making you an invaluable asset in data-driven industries.

Designing Graphical Causal Bayesian Networks in Python

#The code for the simulation has alread been done

# Create a DataFrame from the generated boolean arrays
df = pd.DataFrame({"Cloudy": cloudy, "Sprinkler": sprinkler, "Rain": rain, "Wet_Grass": wet_grass})

# Define the network structure
sm = StructureModel()

# adding  the edges 
sm.add_edges_from([
         ('Cloudy', 'Sprinkler'),
         ('Cloudy', 'Rain'),
         ('Sprinkler', 'Wet_Grass'),
         ('Rain', 'Wet_Grass')])

#Create the model
bn = BayesianNetwork(sm)

#use the df (columns) to create the nodes  of the network
bn = bn.fit_node_states(df)

#fit the model with the train set
bn = bn.fit_cpds(df, method="BayesianEstimator", bayes_prior="K2")

# Create the query 
ie = InferenceEngine(bn)
marginals = ie.query()

# Query the baseline
baseline = ie.query({})


#Round the Baseline
rounded_baseline = {
    outer_key: {inner_key: round(value, 2) for inner_key, value in inner_dict.items()}
    for outer_key, inner_dict in baseline.items()}

print(rounded_baseline)


def test(n={}):
  return rounded_baseline


import pandas as pd
import numpy as np
from causalnex.structure import StructureModel
from causalnex.network import BayesianNetwork
from causalnex.inference import InferenceEngine

n_rows = 100000

cloudy_prob = 0.49
sprinkler_prob = 0.73
rain_prob = 0.49
wet_grass_prob = 0.86

# Generate random boolean values based on the given probabilities
cloudy = np.random.choice([True, False], size=n_rows, p=[cloudy_prob, 1 - cloudy_prob])
sprinkler = np.random.choice([True, False], size=n_rows, p=[sprinkler_prob, 1 - sprinkler_prob])
rain = np.random.choice([True, False], size=n_rows, p=[rain_prob, 1 - rain_prob])
wet_grass = np.random.choice([True, False], size=n_rows, p=[wet_grass_prob, 1 - wet_grass_prob])

# Create a DataFrame from the generated boolean arrays
df = pd.DataFrame({"Cloudy": cloudy, "Sprinkler": sprinkler, "Rain": rain, "Wet_Grass": wet_grass})

# Define the network structure
sm = StructureModel()

# adding  the edges 
sm.add_edges_from([
         ('Cloudy', 'Sprinkler'),
         ('Cloudy', 'Rain'),
         ('Sprinkler', 'Wet_Grass'),
         ('Rain', 'Wet_Grass')])

#Create the model
bn = BayesianNetwork(sm)

#use the df (columns) to create the nodes  of the network
bn = bn.fit_node_states(df)

#fit the model with the train set
bn = bn.fit_cpds(df, method="BayesianEstimator", bayes_prior="K2")

# Create the query 
ie = InferenceEngine(bn)
marginals = ie.query()

# Query the baseline
baseline = ie.query({})


#Round the Baseline
rounded_baseline = {
    outer_key: {inner_key: round(value, 2) for inner_key, value in inner_dict.items()}
    for outer_key, inner_dict in baseline.items()}

print(rounded_baseline)



def test(n={}):
  return rounded_baseline


Understand the steps to create a simple Bayesian network fed with simulated data in Python.

Introduction to Graphs

Exploring Graphs Characteristics in Python

Bayesian Networks

Graph Patterns in Bayesian Networks

Structure-Based Hyperparameters in Bayesian Network

Building a Graph-Based Bayesian Network

Data Based Bayesian Networks

Building a Complex Bayesian Network

Create a Knowledge and Data-Based Bayesian Network

Evaluating the Output and Performance

Conclusion

Solution: Create a Bayesian Network Using Simulated Data

The solution to the exercise