cur_params: Current values of all model parameters
In jaegea/MEIGOR: MEIGOR - MEtaheuristics for bIoinformatics Global Optimization
cur_params R Documentation
Current values of all model parameters
Description
For a given set of values for the parameters to be estimated,
this method returns an array containing the actual (not log-transformed)
values of all model parameters, not just those to be estimated,
in the same order as specified in the model. This is helpful
when simulating the model at a given position in parameter space.
Usage
cur_params(output, options, position = NULL)
Arguments
options
list with entries as explained below.
Options set – defines the problem and sets some
parameters to control the MCMC algorithm.
model: List of model parameters - to estimate.
The parameter objects must each have a
'value' attribute containing the parameter's numerical value.
estimate_params: list.
List of parameters to estimate, all of which must also be listed
in 'options$model$parameters'.
initial_values: list of float, optional.
Starting values for parameters to estimate. If omitted, will use
the nominal values from 'options$model$parameters'
step_fn: callable f(output), optional.
User callback, called on every MCMC iteration.
likelihood_fn: callable f(output, position).
User likelihood function.
prior_fn: callable f(output, position), optional.
User prior function. If omitted, a flat prior will be used.
nsteps: int.
Number of MCMC iterations to perform.
use_hessian: logical, optional.
Wheter to use the Hessian to guide the walk. Defaults to FALSE.
rtol: float or list of float, optional.
Relative tolerance for ode solver.
atol: float or list of float, optional.
Absolute tolerance for ode solver.
norm_step_size: float, optional.
MCMC step size. Defaults to a reasonable value.
hessian_period: int, optional.
Number of MCMC steps between Hessian recalculations. Defaults
to a reasonable but fairly large value, as Hessian calculation is expensive.
hessian_scale: float, optional.
Scaling factor used in generating Hessian-guided steps. Defaults to a
reasonable value.
sigma_adj_interval: int, optional.
How often to adjust 'output$sig_value' while annealing to meet
'accept_rate_target'. Defaults to a reasonable value.
anneal_length: int, optional.
Length of initial "burn-in" annealing period. Defaults to 10
'nsteps', or if 'use_hessian' is TRUE, to 'hessian_period' (i.e.
anneal until first hessian is calculated)
T_init: float, optional.
Initial temperature for annealing. Defaults to a resonable value.
accept_rate_target: float, optional.
Desired acceptance rate during annealing. Defaults to a reasonable value.
See also 'sigma_adj_interval' above.
sigma_max: float, optional.
Maximum value for 'output$sig_value'. Defaults to a resonable value.
sigma_min: float, optional.
Minimum value for 'output$sig_value'. Defaults to a resonable value.
sigma_step: float, optional.
Increment for 'output$sig_value' adjustments. Defaults to a resonable
value. To eliminate adaptive step size, set sigma_step to 1.
thermo_temp: float in the range [0,1], optional.
Temperature for thermodynamic integration support. Used to scale
likelihood when calculating the posterior value. Defaults to 1,
i.e. no effect.
output
List of output values with entries as explained below.
num_estimate: int.
Number of parameters to estimate.
estimate_idx: list of int.
Indices of parameters to estimate in the model's full parameter list.
initial_values: list of float.
Starting values for parameters to estimate, taken from the parameters'
nominal values in the model or explicitly specified in 'options'.
initial_position: list of float.
Starting position of the MCMC walk in parameter space (log10 of 'initial_values').
position: list of float.
Current position of MCMC walk in parameter space, i.e. the most
recently accepted move.
test_position: list of float.
Proposed MCMC mmove.
acceptance: int.
Number of accepted moves.
T: float.
Current value of the simulated annealing temperature.
T_decay: float.
Constant for exponential decay of 'T', automatically calculated such
that T will decay from 'options$T_init' down to 1 over the first
'options$anneal_length' setps.
sig_value: float.
Current value of sigma, the scaling factor for the proposal distribution.
The MCMC algorithm dynamically tunes this to maintain the aaceptance
rate specified in 'options$accept_rate_target'.
iter: int.
Current MCMC step number.
start_iter: int.
Starting MCMC step number.
ode_options: list.
Options for the ODE integrator, currently just 'rtol' for relative
tolerance and 'atol' for absolute tolerance.
initial_prior: float.
Starting prior value, i.e. the value at 'initial_position'.
initial_likelihood: float.
Starting likelihood value, i.e. the value at 'initial_position'.
initial_posterior: float.
Starting posterior value, i.e. the value at 'initial_position'.
accept_prior: float.
Current prior value i.e. the value at 'position'.
accept_likelihood: float.
Current likelihood value i.e. the value at 'position'.
accept_posterior: float.
Current posterior value i.e. the value at 'position'.
test_prior: float.
Prior value at 'test_position'.
test_likelihood: float.
Likelihood value at 'test_position'.
test_posterior: float.
Posterior value at 'test_position'.
hessian: array of float.
Current hessian of the posterior landscape. Size is
'num_estimate' x 'num_estimate'.
positions: array of float.
Trace of all proposed moves. Size is 'num_estimate' x 'nsteps'.
priors: array of float.
Trace of all priors corresponding to 'positions'. Length is 'nsteps'.
likelihoods: array of float.
Trace of all likelihoods corresponding to 'positions'. Length is 'nsteps'.
posteriors: array of float.
Trace of all posteriors corresponding to 'positions'. Length is 'nsteps'.
alphas: array of float. Trace of 'alpha' parameter and calculated values. Length is 'nsteps'.
sigmas: array of float. Trace of 'sigma' parameter and calculated values. Length is 'nsteps'.
delta_posteriors: array of float. Trace of 'delta_posterior' parameter and calculated values. Length is 'nsteps'.
ts: array of float. Trace of 'T' parameter and calculated values. Length is 'nsteps'.
accepts: logical array.
Trace of wheter each proposed move was accepted or not.
Length is 'nsteps'.
rejects: logical array. Trace of wheter each proposed move was rejected or not. Length is 'nsteps'.
hessians: array of float.
Trace of all hessians. Size is 'num_estimate' x 'num_estimate' x 'num_hessians'
where 'num_hessians' is the actual number of hessians to be calculated.
position
list of float, optional.
log10 of the values of the parameters being estimated.
If omitted, 'output$position' (the most recent accepted output move)
will be used. The model's nominal values will be used for all parameters
*not* being estimated, regardless.
Value
A list of the values of all model parameters.
Examples
data("simpleExample", package="MEIGOR")
initial_pars = createLBodeContPars(model, LB_n=1, LB_k=0.1, LB_tau=0.01, UB_n=5, UB_k=0.9, UB_tau=10, random=TRUE)
simData = plotLBodeFitness(cnolist, model, initial_pars, reltol=1e-05, atol=1e-03, maxStepSize=0.01)
f_bayesFit <- function(position, params=initial_pars, exp_var=opts$exp_var) {
# convert from log
params$parValues = 10^position
ysim = getLBodeDataSim(cnolist=cnolist, model=model,
ode_parameters=params)
data_as_vec = unlist(cnolist$valueSignals)
sim_as_vec = unlist(ysim)
# set nan (NAs) to 0
sim_as_vec[is.na(sim_as_vec)] = 0
sim_as_vec[is.nan(sim_as_vec)]= 0
return(sum((data_as_vec-sim_as_vec)^2/(2*exp_var^2)))
}
prior_mean = log10(initial_pars$parValues)
prior_var = 10
opts <- list("model"=NULL, "estimate_params"=NULL,"initial_values"=NULL,
"tspan"=NULL, "step_fn"=NULL, "likelihood_fn"=NULL,
"prior_fn"=NULL, "nsteps"=NULL, "use_hessian"=FALSE,
"rtol"=NULL, "atol"=NULL, "norm_step_size"=0.75,
"hessian_period"=25000, "hessian_scale"=0.085,
"sigma_adj_interval"=NULL, "anneal_length"=NULL,
"T_init"=10, "accept_rate_target"=0.3, "sigma_max"=1,
"sigma_min"=0.25, "sigma_step"=0.125, "thermo_temp"=1, "seed"=NULL)
opts$nsteps = 2000
opts$likelihood_fn = f_bayesFit
opts$use_hessian = TRUE
opts$hessian_period = opts$nsteps/10
opts$model = list(parameters=list(name=initial_pars$parNames,
value=initial_pars$parValues))
opts$estimate_params = initial_pars$parValues
opts$exp_var = 0.01
res = runBayesFit(opts)
initial_pars$parValues = cur_params(output=res, options=opts)
jaegea/MEIGOR documentation built on April 8, 2024, 9:36 a.m.
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| cur_params | R Documentation |
Current values of all model parameters
Description
For a given set of values for the parameters to be estimated, this method returns an array containing the actual (not log-transformed) values of all model parameters, not just those to be estimated, in the same order as specified in the model. This is helpful when simulating the model at a given position in parameter space.
Usage
cur_params(output, options, position = NULL)
Arguments
options |
list with entries as explained below. Options set – defines the problem and sets some parameters to control the MCMC algorithm. model: List of model parameters - to estimate. The parameter objects must each have a 'value' attribute containing the parameter's numerical value. estimate_params: list. List of parameters to estimate, all of which must also be listed in 'options$model$parameters'. initial_values: list of float, optional. Starting values for parameters to estimate. If omitted, will use the nominal values from 'options$model$parameters' step_fn: callable f(output), optional. User callback, called on every MCMC iteration. likelihood_fn: callable f(output, position). User likelihood function. prior_fn: callable f(output, position), optional. User prior function. If omitted, a flat prior will be used. nsteps: int. Number of MCMC iterations to perform. use_hessian: logical, optional. Wheter to use the Hessian to guide the walk. Defaults to FALSE. rtol: float or list of float, optional. Relative tolerance for ode solver. atol: float or list of float, optional. Absolute tolerance for ode solver. norm_step_size: float, optional. MCMC step size. Defaults to a reasonable value. hessian_period: int, optional. Number of MCMC steps between Hessian recalculations. Defaults to a reasonable but fairly large value, as Hessian calculation is expensive. hessian_scale: float, optional. Scaling factor used in generating Hessian-guided steps. Defaults to a reasonable value. sigma_adj_interval: int, optional. How often to adjust 'output$sig_value' while annealing to meet 'accept_rate_target'. Defaults to a reasonable value. anneal_length: int, optional. Length of initial "burn-in" annealing period. Defaults to 10 'nsteps', or if 'use_hessian' is TRUE, to 'hessian_period' (i.e. anneal until first hessian is calculated) T_init: float, optional. Initial temperature for annealing. Defaults to a resonable value. accept_rate_target: float, optional. Desired acceptance rate during annealing. Defaults to a reasonable value. See also 'sigma_adj_interval' above. sigma_max: float, optional. Maximum value for 'output$sig_value'. Defaults to a resonable value. sigma_min: float, optional. Minimum value for 'output$sig_value'. Defaults to a resonable value. sigma_step: float, optional. Increment for 'output$sig_value' adjustments. Defaults to a resonable value. To eliminate adaptive step size, set sigma_step to 1. thermo_temp: float in the range [0,1], optional. Temperature for thermodynamic integration support. Used to scale likelihood when calculating the posterior value. Defaults to 1, i.e. no effect. |
output |
List of output values with entries as explained below. num_estimate: int. Number of parameters to estimate. estimate_idx: list of int. Indices of parameters to estimate in the model's full parameter list. initial_values: list of float. Starting values for parameters to estimate, taken from the parameters' nominal values in the model or explicitly specified in 'options'. initial_position: list of float. Starting position of the MCMC walk in parameter space (log10 of 'initial_values'). position: list of float. Current position of MCMC walk in parameter space, i.e. the most recently accepted move. test_position: list of float. Proposed MCMC mmove. acceptance: int. Number of accepted moves. T: float. Current value of the simulated annealing temperature. T_decay: float. Constant for exponential decay of 'T', automatically calculated such that T will decay from 'options$T_init' down to 1 over the first 'options$anneal_length' setps. sig_value: float. Current value of sigma, the scaling factor for the proposal distribution. The MCMC algorithm dynamically tunes this to maintain the aaceptance rate specified in 'options$accept_rate_target'. iter: int. Current MCMC step number. start_iter: int. Starting MCMC step number. ode_options: list. Options for the ODE integrator, currently just 'rtol' for relative tolerance and 'atol' for absolute tolerance. initial_prior: float. Starting prior value, i.e. the value at 'initial_position'. initial_likelihood: float. Starting likelihood value, i.e. the value at 'initial_position'. initial_posterior: float. Starting posterior value, i.e. the value at 'initial_position'. accept_prior: float. Current prior value i.e. the value at 'position'. accept_likelihood: float. Current likelihood value i.e. the value at 'position'. accept_posterior: float. Current posterior value i.e. the value at 'position'. test_prior: float. Prior value at 'test_position'. test_likelihood: float. Likelihood value at 'test_position'. test_posterior: float. Posterior value at 'test_position'. hessian: array of float. Current hessian of the posterior landscape. Size is 'num_estimate' x 'num_estimate'. positions: array of float. Trace of all proposed moves. Size is 'num_estimate' x 'nsteps'. priors: array of float. Trace of all priors corresponding to 'positions'. Length is 'nsteps'. likelihoods: array of float. Trace of all likelihoods corresponding to 'positions'. Length is 'nsteps'. posteriors: array of float. Trace of all posteriors corresponding to 'positions'. Length is 'nsteps'. alphas: array of float. Trace of 'alpha' parameter and calculated values. Length is 'nsteps'. sigmas: array of float. Trace of 'sigma' parameter and calculated values. Length is 'nsteps'. delta_posteriors: array of float. Trace of 'delta_posterior' parameter and calculated values. Length is 'nsteps'. ts: array of float. Trace of 'T' parameter and calculated values. Length is 'nsteps'. accepts: logical array. Trace of wheter each proposed move was accepted or not. Length is 'nsteps'. rejects: logical array. Trace of wheter each proposed move was rejected or not. Length is 'nsteps'. hessians: array of float. Trace of all hessians. Size is 'num_estimate' x 'num_estimate' x 'num_hessians' where 'num_hessians' is the actual number of hessians to be calculated. |
position |
list of float, optional. log10 of the values of the parameters being estimated. If omitted, 'output$position' (the most recent accepted output move) will be used. The model's nominal values will be used for all parameters *not* being estimated, regardless. |
Value
A list of the values of all model parameters.
Examples
data("simpleExample", package="MEIGOR")
initial_pars = createLBodeContPars(model, LB_n=1, LB_k=0.1, LB_tau=0.01, UB_n=5, UB_k=0.9, UB_tau=10, random=TRUE)
simData = plotLBodeFitness(cnolist, model, initial_pars, reltol=1e-05, atol=1e-03, maxStepSize=0.01)
f_bayesFit <- function(position, params=initial_pars, exp_var=opts$exp_var) {
# convert from log
params$parValues = 10^position
ysim = getLBodeDataSim(cnolist=cnolist, model=model,
ode_parameters=params)
data_as_vec = unlist(cnolist$valueSignals)
sim_as_vec = unlist(ysim)
# set nan (NAs) to 0
sim_as_vec[is.na(sim_as_vec)] = 0
sim_as_vec[is.nan(sim_as_vec)]= 0
return(sum((data_as_vec-sim_as_vec)^2/(2*exp_var^2)))
}
prior_mean = log10(initial_pars$parValues)
prior_var = 10
opts <- list("model"=NULL, "estimate_params"=NULL,"initial_values"=NULL,
"tspan"=NULL, "step_fn"=NULL, "likelihood_fn"=NULL,
"prior_fn"=NULL, "nsteps"=NULL, "use_hessian"=FALSE,
"rtol"=NULL, "atol"=NULL, "norm_step_size"=0.75,
"hessian_period"=25000, "hessian_scale"=0.085,
"sigma_adj_interval"=NULL, "anneal_length"=NULL,
"T_init"=10, "accept_rate_target"=0.3, "sigma_max"=1,
"sigma_min"=0.25, "sigma_step"=0.125, "thermo_temp"=1, "seed"=NULL)
opts$nsteps = 2000
opts$likelihood_fn = f_bayesFit
opts$use_hessian = TRUE
opts$hessian_period = opts$nsteps/10
opts$model = list(parameters=list(name=initial_pars$parNames,
value=initial_pars$parValues))
opts$estimate_params = initial_pars$parValues
opts$exp_var = 0.01
res = runBayesFit(opts)
initial_pars$parValues = cur_params(output=res, options=opts)
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