mlrmbo_optimization: Optimization of objective functions via model-based...

In saeyslab/nichenetr: Modeling Intercellular Communication by Linking Ligands to Target Genes with NicheNet

Optimization of objective functions via model-based optimization (mlrMBO).

Description

mlrmbo_optimization will execute multi-objective model-based optimization of an objective function. The defined surrogate learner here is "kriging".

Usage

mlrmbo_optimization(run_id,obj_fun,niter,ncores,nstart,additional_arguments)

Arguments

run_id	Indicate the id of the optimization run.
obj_fun	An objective function as created by the function mlrMBO::makeMultiObjectiveFunction.
niter	The number of iterations during the optimization process.
ncores	The number of cores on which several parameter settings will be evaluated in parallel.
nstart	The number of different parameter settings used in the begin design.
additional_arguments	A list of named additional arguments that will be passed on the objective function.

Value

A result object from the function mlrMBO::mbo. Among other things, this contains the optimal parameter settings, the output corresponding to every input etc.

Examples

## Not run: 
library(dplyr)
library(mlrMBO)
library(parallelMap)
additional_arguments_topology_correction = list(source_names = source_weights_df$source %>% unique(), algorithm = "PPR", correct_topology = TRUE,lr_network = lr_network, sig_network = sig_network, gr_network = gr_network, settings = lapply(expression_settings_validation,convert_expression_settings_evaluation), secondary_targets = FALSE, remove_direct_links = "no", cutoff_method = "quantile")
nr_datasources = additional_arguments_topology_correction$source_names %>% length()

obj_fun_multi_topology_correction = makeMultiObjectiveFunction(name = "nichenet_optimization",description = "data source weight and hyperparameter optimization: expensive black-box function", fn = model_evaluation_optimization, par.set = makeParamSet( makeNumericVectorParam("source_weights", len = nr_datasources, lower = 0, upper = 1), makeNumericVectorParam("lr_sig_hub", len = 1, lower = 0, upper = 1),  makeNumericVectorParam("gr_hub", len = 1, lower = 0, upper = 1),  makeNumericVectorParam("damping_factor", len = 1, lower = 0, upper = 0.99)), has.simple.signature = FALSE,n.objectives = 4, noisy = FALSE,minimize = c(FALSE,FALSE,FALSE,FALSE))

mlrmbo_optimization = lapply(1,mlrmbo_optimization, obj_fun = obj_fun_multi_topology_correction, niter = 3, ncores = 8, nstart = 100, additional_arguments = additional_arguments_topology_correction)


## End(Not run)

saeyslab/nichenetr documentation built on Sept. 7, 2024, 10:27 a.m.