BEAST v2.7.8 Documentation: beast.base.evolution.operator.AdaptableOperatorSampler

Proposes a move in state space.
An operator which selects samples from a series of other operators, with respect to their ability to explore one or more real/int parameters Training for each operator occurs following a burn-in period After a learning period, AdaptableOperatorSampler should pick the operator which is giving the best results n a particular data set

Reference:

Douglas J, Zhang R, Bouckaert R. Adaptive dating and fast proposals: Revisiting the phylogenetic relaxed clock model. PLoS computational biology. 2021 Feb 2;17(2):e1008322.

doi:10.1371/journal.pcbi.1008322

Inputs:

parameter, tree, operator, burnin, learnin, uniformp, metric, maxRuntime, dimensional, weight

 

parameter
type: beast.base.core.Function***
list of parameters to compare before and after the proposal. If the tree heights are a parameter then include the tree under 'tree'
Optional input

 

tree
type: beast.base.evolution.tree.Tree***
tree(s) containing node heights to compare before and after the proposal (optional)
Optional input

 

operator
type: beast.base.inference.Operator***
list of operators to select from
Optional input

 

burnin
type: java.lang.Integer
number of operator calls until the learning process begins (default: 1000)
Optional input. Default: 1000

 

learnin
type: java.lang.Integer
number of operator calls after learning begins (ie. at burnin) but before this operator starts to use what it has learned (default: 100 x number of operators)
Optional input

 

uniformp
type: java.lang.Double
the probability that operators are sampled uniformly at random instead of using the trained parameters (default 0.1)
Optional input. Default: 0.1

 

metric
type: beast.base.evolution.tree.TreeMetric
A function for computing the distance between trees. If left empty, then tree distances will not be compared
Optional input

 

maxRuntime
type: java.lang.Double
The maximum amount of time (ms) to count towards the runtime of an operator. This should ensure that indefinite thread interruptions,eg. on a cluster, do not unfairly penalise an operator for being slow
Optional input. Default: 1000000.0

 

dimensional
type: java.lang.Boolean
Whether to set the weight of this operator as the dimension of its parameter
Optional input. Default: false

 

weight
type: java.lang.Double
weight with which this operator is selected
Required input