Structure Learning Hyperparameters¶
This table below describes the hyperparameters that are supported by one or more CausalIQ structure learning algorithms.
| Hyperparameter | Type | Description |
|---|---|---|
| alpha | constraint | p-value threshold used in Conditional Independence tests |
| ci_test | constraint | Conditional Independence tests used in constraint-based learning |
| iss | constraint | Imaginary Sample Size used to weight priors in Bayesian scores |
| max_elapsed | general | Maximum elapsed time allowed in seconds |
| max_iterations | score | Limit on the number of iterations in score-based learning |
| no_increase | score | Number of iterations allowed where the score does not increase |
| penalty_weight | score | Weighting assigned to the penalty component of scores such as AIC and BIC |
| score | score | Objective score used in score-based learning |
| tabulist_len | score | Length of the Tabu list |
General hyperparameters¶
max_elapsed¶
Maximum allowed elapsed execution time in seconds specified as a positive integer.
Score-based hyperparameters¶
iss¶
A real value defining the Imaginary Sample Size (ISS) used to weight the prior in Bayesian scores.
max_iterations¶
A positive integer placing a limit on the number of iterations.
no_increase¶
A non-negative integer if iterations where the score is allowed not to increase.
penalty_weight¶
A positive real number weighting the penalty component of the AIC and BIC scores relative to the log. likelihood.
score¶
Defines the objective score used by score-based algorithms and the score-based phases of hybrid algorithms. Supported values are:
aic: Aikike Information Criterionbdeu: Bayesian Equivalent Uniformbge: Bayesian Gaussian Equivalentbic: the Bayesian Information Criterionk2: K2loglik: Log Likelihood
tabulist_len¶
A positive integer defining the length of the Tabu list which contains the highest-scoring recently visited graphs.
Constraint-based hyperparameters¶
alpha¶
A positive real-value below 1.0 which defines the p-value threshold below which the CI test is interpreted as indication conditional independence.
ci_test¶
Defines the conditional independence (CI) test used by constraint-based algorithms and the constraint-based phases of hybrid algorithms. Supported values are:
mi: Mutual Independence testx2: Chi-squared test