Package edu.cmu.tetrad.search.blocks

Class TscTestBlockDiscoverer

java.lang.Object

edu.cmu.tetrad.search.blocks.TscTestBlockDiscoverer

All Implemented Interfaces:

BlockDiscoverer

public class TscTestBlockDiscoverer
extends Object
implements BlockDiscoverer

The TscTestBlockDiscoverer class is an implementation of the BlockDiscoverer interface that utilizes the Testing Strong Causal structures (TSC) algorithm to identify and discover clusters or blocks of variables from a given dataset. These blocks represent groups of variables that exhibit strong causal relationships.

This class supports functionality to validate, canonicalize, and apply policies to discovered blocks, ensuring consistency and compliance with specified single cluster policies. It operates using parameters such as statistical significance level (alpha), expected sample size (ess), and a policy for handling overlapping clusters.

Constructor Summary

Constructors

Constructor	Description
TscTestBlockDiscoverer(DataSet dataSet, double alpha, int ess, double ridge, int rMax, SingleClusterPolicy policy, int minRedundancy, boolean verbose)	Constructs a TscTestBlockDiscoverer instance with the specified parameters for discovering and analyzing blocks of variables in a dataset using the TSC algorithm.

Method Summary

Modifier and Type	Method	Description
BlockSpec	discover()	Discovers and identifies blocks of variables using the TSC (Testing Strong Causal structures) algorithm.

Methods inherited from class java.lang.Object

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

Constructor Details

TscTestBlockDiscoverer

public TscTestBlockDiscoverer(DataSet dataSet,
 double alpha,
 int ess,
 double ridge,
 int rMax,
 SingleClusterPolicy policy,
 int minRedundancy,
 boolean verbose)

Constructs a TscTestBlockDiscoverer instance with the specified parameters for discovering and analyzing blocks of variables in a dataset using the TSC algorithm.

Parameters:

dataSet - The dataset to process for discovering variable relationships.

alpha - A significance level parameter used for hypothesis testing, must be in the range (0, 1).

ess - The equivalent sample size used in certain scoring or prior adjustments.

ridge - A regularization parameter for ridge regression; must be greater than or equal to 0.

rMax - The maximum number of iterations or search radius used in the discovery process.

policy - The single cluster policy dictating the criteria for canonicalizing discovered clusters.

minRedundancy - The minimum allowed redundancy level among variables in discovered clusters.

verbose - A flag indicating whether to output detailed logging or debugging information during processing.

Throws:

IllegalArgumentException - If the ridge parameter is less than 0.

Method Details

discover

public BlockSpec discover()

Discovers and identifies blocks of variables using the TSC (Testing Strong Causal structures) algorithm.

This method applies the following steps: - Validates the alpha parameter to ensure it is within the range (0, 1). - Constructs a TscScored instance to compute clusters of variables based on the given dataset. - Ensures discovered clusters (blocks) are valid, canonicalized, and consistent with the specified single cluster policy. - Converts the final block structure to the BlockSpec format for further processing or use.

Specified by:

discover in interface BlockDiscoverer

Returns:

The discovered BlockSpec which represents clusters of variables based on the TSC algorithm.

Throws:

IllegalArgumentException - If the alpha parameter is not in the range (0, 1).