Package edu.cmu.tetrad.search.blocks

Class BpcBlockDiscoverer

java.lang.Object

edu.cmu.tetrad.search.blocks.BpcBlockDiscoverer

All Implemented Interfaces:

BlockDiscoverer

public class BpcBlockDiscoverer
extends Object
implements BlockDiscoverer

The BpcBlockDiscoverer class is an implementation of the BlockDiscoverer interface, designed to discover clusters or "blocks" of indices within a dataset using the BPC (Bayesian Partitioning for Causal Discovery) algorithm. This class leverages statistical testing and clustering policies to identify and refine meaningful groupings of variables.

The discovery process involves utilizing BPC with a specified statistical test, significance threshold, and equivalent sample size to generate initial clusters. These clusters are then adjusted and validated according to predefined policies and canonicalization techniques.

Core functionality: - Discovers blocks of indices from a dataset using the BPC algorithm. - Validates and canonicalizes the resulting clusters to ensure consistency and correctness. - Applies a single-cluster policy to manage merging or refinement of blocks based on the given policy.

Constructor parameters: - dataSet: The data on which block discovery is performed. - ntadTest: The statistical test used by the BPC algorithm. - alpha: The significance threshold for the statistical test in BPC. - ess: The equivalent sample size parameter used in the BPC algorithm. - policy: The policy applied to adjust or refine single clusters during block discovery.

Constructor Summary

Constructors

Constructor	Description
BpcBlockDiscoverer(DataSet dataSet, double alpha, int ess, SingleClusterPolicy policy, boolean verbose)	Constructor for the BpcBlockDiscoverer class, responsible for initiating the discovery of clusters or blocks of indices within a dataset using the BPC (Bayesian Partitioning for Causal Discovery) algorithm.

Method Summary

Modifier and Type	Method	Description
BlockSpec	discover()	Discovers and returns the specification of blocks (clusters of indices) within the dataset using the Bayesian Partitioning for Causal Discovery (BPC) algorithm.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

BpcBlockDiscoverer

public BpcBlockDiscoverer(DataSet dataSet,
 double alpha,
 int ess,
 SingleClusterPolicy policy,
 boolean verbose)

Constructor for the BpcBlockDiscoverer class, responsible for initiating the discovery of clusters or blocks of indices within a dataset using the BPC (Bayesian Partitioning for Causal Discovery) algorithm.

Parameters:

dataSet - the dataset on which block discovery is performed

alpha - the significance threshold for the statistical test in BPC

ess - the equivalent sample size parameter used in the BPC algorithm

policy - the policy applied to adjust or refine single clusters during block discovery

verbose - flag indicating whether verbose output should be generated during the discovery process

Method Details

discover

public BlockSpec discover()

Discovers and returns the specification of blocks (clusters of indices) within the dataset using the Bayesian Partitioning for Causal Discovery (BPC) algorithm.

The method: - Applies the BPC algorithm to find initial clusters based on the provided dataset, statistical test, significance threshold, and equivalent sample size. - Canonicalizes the discovered clusters for consistency. - Refines the clusters using the defined single-cluster policy. - Converts the resulting clusters into a BlockSpec object for further use.

Specified by:

discover in interface BlockDiscoverer

Returns:

the discovered block specification, which encapsulates the refined and canonicalized cluster structure