Bayesian Networks

BaseNetwork class, Hill Climbing and Evolutionary Algorithms

BaseNetwork class

All three BN types are based on an abstract class BaseNetwork. This class provides the basic functions for all BN types. The three BN types are DiscreteBN, ContinuousBN and HybridBN. The HybridBN is a BN that contains both discrete and continuous variables. The DiscreteBN and ContinuousBN are two BN types that are used to represent the BNs that contain only discrete or continuous variables, respectively.

Hill Climbing and Evolutionary Algorithms

Currently BAMT employs Hill Climbing and Evolutionary Algorithms to learn the structure of the BNs. To use them, you need to specify the optimizer parameter in add_edges method. Here is an example:

For Example:

import bamt.networks as networks
import bamt.preprocessors as pp
import pandas as pd

asia = pd.read_csv('data.csv')
encoder = preprocessing.LabelEncoder()
discretizer = preprocessing.KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='quantile')

p = pp.Preprocessor([('encoder', encoder), ('discretizer', discretizer)])
discretized_data, est = p.apply(asia)

bn = networks.DiscreteBN()
info = p.info
info

# add edges using Hill Climbing
bn.add_edges(discretized_data, optimizer='HC')
# add edges using Evolutionary Algorithm
bn.add_edges(discretized_data, optimizer='Evo')

Evolutionary Algorithm has these additional parameters:

param data:

The data from which to build the structure.

type data:

DataFrame

param classifier:

A classification model for discrete nodes, defaults to None.

type classifier:

Optional[object]

param regressor:

A regression model for continuous nodes, defaults to None.

type regressor:

Optional[object]

Keyword Args:

  • init_nodes (list) – Initial nodes to be included in the population.

  • max_arity (int) – Maximum arity for the evolutionary algorithm.

  • timeout (int) – Timeout for the evolutionary algorithm in minutes.

  • pop_size (int) – Population size for the evolutionary algorithm.

  • crossover_prob (float) – Crossover probability for the evolutionary algorithm.

  • mutation_prob (float) – Mutation probability for the evolutionary algorithm.

  • custom_mutations (list) – Custom mutation types for the evolutionary algorithm.

  • custom_crossovers (list) – Custom crossover types for the evolutionary algorithm.

  • selection_type (SelectionTypesEnum) – Selection type for the evolutionary algorithm.

  • blacklist (list) – Blacklist for the evolutionary algorithm.

  • whitelist (list) – Whitelist for the evolutionary algorithm.

  • custom_constraints (list) – Custom constraints for the evolutionary algorithm.

  • custom_metric (function) – Custom objective metric for the evolutionary algorithm.

The resulting structure is stored in the skeleton attribute of the EvoStructureBuilder object.

HillClimbing parameters are described below in DiscreteBN, ContinuousBN and HybridBN sections.