SimulationManager: R6 class representing a simulation manager.
In poems: Pattern-Oriented Ensemble Modeling System
SimulationManager R Documentation
R6 class representing a simulation manager.
Description
R6 class to represent a manager for running multiple model
simulations and saving results.
Super classes
poems::GenericClass -> poems::GenericManager -> SimulationManager
Public fields
attachedA list of dynamically attached attributes (name-value pairs).
Active bindings
sample_dataA data frame of sampled parameters for each simulation/result.
model_templateA SimulationModel (or inherited class) object with parameters common to all simulations.
nested_modelA SimulationModel (or inherited class) object with empty sample parameters and a nested model template common to all simulations.
generatorsA list of generators (Generator or inherited class) objects for generating simulation model values.
model_simulatorA ModelSimulator (or inherited class) object for running the simulations.
parallel_coresNumber of cores for running the simulations in parallel.
results_dirResults directory path.
results_extResult file extension (default is .RData).
results_filename_attributesA vector of: prefix (optional); attribute names (from the sample data frame); postfix (optional); utilized to construct results filenames.
error_messagesA vector of error messages encountered when setting model attributes.
warning_messagesA vector of warning messages encountered when setting model attributes.
Methods
Public methods
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Inherited methods
Method new()
Initialization method sets any included attributes (sample_data, model_template, generators, model_simulator, parallel_cores, results_dir, results_filename_attributes) and attaches other attributes individually listed.
Usage
SimulationManager$new(model_template = NULL, ...)
Arguments
model_templateA SimulationModel (or inherited class) object with parameters common to all simulations.
...Parameters listed individually.
Method run()
Runs the multiple population simulations (via the set function), stores the results, and creates/writes a simulation log.
Usage
SimulationManager$run(results_dir = NULL)
Arguments
results_dirResults directory path (must be present if not already set within manager class object).
Returns
Simulator log as a list.
Method set_model_sample()
Sets the model sample attributes via the sample data frame and the generators.
Usage
SimulationManager$set_model_sample(model, sample_index)
Arguments
modelSimulationModel (or inherited class) object (clone) to receive sample attributes.
sample_indexIndex of sample from data frame.
Method log_simulation()
Summarizes the simulation log generated within the run method and writes it to a text file in the results directory.
Usage
SimulationManager$log_simulation(simulation_log)
Arguments
simulation_logNested list of simulation log entries generated via the run method.
Method clone()
The objects of this class are cloneable with this method.
Usage
SimulationManager$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## Not run:
# U Island example region
coordinates <- data.frame(x = rep(seq(177.01, 177.05, 0.01), 5),
y = rep(seq(-18.01, -18.05, -0.01), each = 5))
template_raster <- Region$new(coordinates = coordinates)$region_raster # full extent
template_raster[][-c(7, 9, 12, 14, 17:19)] <- NA # make U Island
region <- Region$new(template_raster = template_raster)
raster::plot(region$region_raster, main = "Example region (indices)",
xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
colNA = "blue")
# Example population model template
model_template <- PopulationModel$new(region = region,
time_steps = 10, # years
populations = region$region_cells, # 7
stage_matrix = 1)
# Example generators for initial abundance and carrying capacity
hs_matrix <- c(0.5, 0.3, 0.7, 0.9, 0.6, 0.7, 0.8)
initial_gen <- Generator$new(description = "initial abundance",
region = region,
hs_matrix = hs_matrix, # template attached
inputs = c("initial_n"),
outputs = c("initial_abundance"))
initial_gen$add_generative_requirements(list(initial_abundance = "function"))
initial_gen$add_function_template("initial_abundance",
function_def = function(params) {
stats::rmultinom(1, size = params$initial_n,
prob = params$hs_matrix)[, 1]
},
call_params = c("initial_n", "hs_matrix"))
capacity_gen <- Generator$new(description = "carrying capacity",
region = region,
hs_matrix = hs_matrix, # template attached
inputs = c("density_max"),
outputs = c("carrying_capacity"))
capacity_gen$add_generative_requirements(list(carrying_capacity = "function"))
capacity_gen$add_function_template("carrying_capacity",
function_def = function(params) {
round(params$density_max*params$hs_matrix)
},
call_params = c("density_max", "hs_matrix"))
# Sample input parameters
sample_data <- data.frame(initial_n = c(40, 60, 80), density_max = c(15, 20, 25))
# Simulation manager
sim_manager <- SimulationManager$new(sample_data = sample_data,
model_template = model_template,
generators = list(initial_gen, capacity_gen),
parallel_cores = 2,
results_dir = tempdir())
run_output <- sim_manager$run()
run_output$summary
dir(tempdir(), "*.RData") # includes 3 result files
for (i in 1:3) {
print(paste("Run", i, "results:"))
file_name <- paste0(sim_manager$get_results_filename(i), ".RData")
print(readRDS(file.path(tempdir(), file_name)))
}
dir(tempdir(), "*.txt") # plus simulation log
## End(Not run)
poems documentation built on Oct. 7, 2023, 9:06 a.m.
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| SimulationManager | R Documentation |
R6 class representing a simulation manager.
Description
R6 class to represent a manager for running multiple model
simulations and saving results.
Super classes
poems::GenericClass -> poems::GenericManager -> SimulationManager
Public fields
attachedA list of dynamically attached attributes (name-value pairs).
Active bindings
sample_dataA data frame of sampled parameters for each simulation/result.
model_templateA
SimulationModel(or inherited class) object with parameters common to all simulations.nested_modelA
SimulationModel(or inherited class) object with empty sample parameters and a nested model template common to all simulations.generatorsA list of generators (
Generatoror inherited class) objects for generating simulation model values.model_simulatorA
ModelSimulator(or inherited class) object for running the simulations.parallel_coresNumber of cores for running the simulations in parallel.
results_dirResults directory path.
results_extResult file extension (default is .RData).
results_filename_attributesA vector of: prefix (optional); attribute names (from the sample data frame); postfix (optional); utilized to construct results filenames.
error_messagesA vector of error messages encountered when setting model attributes.
warning_messagesA vector of warning messages encountered when setting model attributes.
Methods
Public methods
Inherited methods
Method new()
Initialization method sets any included attributes (sample_data, model_template, generators, model_simulator, parallel_cores, results_dir, results_filename_attributes) and attaches other attributes individually listed.
Usage
SimulationManager$new(model_template = NULL, ...)
Arguments
model_templateA SimulationModel (or inherited class) object with parameters common to all simulations.
...Parameters listed individually.
Method run()
Runs the multiple population simulations (via the set function), stores the results, and creates/writes a simulation log.
Usage
SimulationManager$run(results_dir = NULL)
Arguments
results_dirResults directory path (must be present if not already set within manager class object).
Returns
Simulator log as a list.
Method set_model_sample()
Sets the model sample attributes via the sample data frame and the generators.
Usage
SimulationManager$set_model_sample(model, sample_index)
Arguments
modelSimulationModel(or inherited class) object (clone) to receive sample attributes.sample_indexIndex of sample from data frame.
Method log_simulation()
Summarizes the simulation log generated within the run method and writes it to a text file in the results directory.
Usage
SimulationManager$log_simulation(simulation_log)
Arguments
simulation_logNested list of simulation log entries generated via the run method.
Method clone()
The objects of this class are cloneable with this method.
Usage
SimulationManager$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## Not run:
# U Island example region
coordinates <- data.frame(x = rep(seq(177.01, 177.05, 0.01), 5),
y = rep(seq(-18.01, -18.05, -0.01), each = 5))
template_raster <- Region$new(coordinates = coordinates)$region_raster # full extent
template_raster[][-c(7, 9, 12, 14, 17:19)] <- NA # make U Island
region <- Region$new(template_raster = template_raster)
raster::plot(region$region_raster, main = "Example region (indices)",
xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
colNA = "blue")
# Example population model template
model_template <- PopulationModel$new(region = region,
time_steps = 10, # years
populations = region$region_cells, # 7
stage_matrix = 1)
# Example generators for initial abundance and carrying capacity
hs_matrix <- c(0.5, 0.3, 0.7, 0.9, 0.6, 0.7, 0.8)
initial_gen <- Generator$new(description = "initial abundance",
region = region,
hs_matrix = hs_matrix, # template attached
inputs = c("initial_n"),
outputs = c("initial_abundance"))
initial_gen$add_generative_requirements(list(initial_abundance = "function"))
initial_gen$add_function_template("initial_abundance",
function_def = function(params) {
stats::rmultinom(1, size = params$initial_n,
prob = params$hs_matrix)[, 1]
},
call_params = c("initial_n", "hs_matrix"))
capacity_gen <- Generator$new(description = "carrying capacity",
region = region,
hs_matrix = hs_matrix, # template attached
inputs = c("density_max"),
outputs = c("carrying_capacity"))
capacity_gen$add_generative_requirements(list(carrying_capacity = "function"))
capacity_gen$add_function_template("carrying_capacity",
function_def = function(params) {
round(params$density_max*params$hs_matrix)
},
call_params = c("density_max", "hs_matrix"))
# Sample input parameters
sample_data <- data.frame(initial_n = c(40, 60, 80), density_max = c(15, 20, 25))
# Simulation manager
sim_manager <- SimulationManager$new(sample_data = sample_data,
model_template = model_template,
generators = list(initial_gen, capacity_gen),
parallel_cores = 2,
results_dir = tempdir())
run_output <- sim_manager$run()
run_output$summary
dir(tempdir(), "*.RData") # includes 3 result files
for (i in 1:3) {
print(paste("Run", i, "results:"))
file_name <- paste0(sim_manager$get_results_filename(i), ".RData")
print(readRDS(file.path(tempdir(), file_name)))
}
dir(tempdir(), "*.txt") # plus simulation log
## End(Not run)
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