R6_hype: Hyperparameter optimization
In comparer: Compare Output and Run Time

R6_hype

R Documentation

Hyperparameter optimization

Description

Hyperparameter optimization

Public fields

X: Data frame of inputs that have been evaluated or will be evaluated next.
Z: Output at X
runtime: The time it took to evaluate each row of X
parnames: Names of the parameters
parlowerraw: Lower bounds for each parameter on raw scale
parupperraw: Upper bounds for each parameter on raw scale
parlowertrans: Lower bounds for each parameter on transformed scale
paruppertrans: Upper bounds for each parameter on transformed scale
parlist: List of all parameters
modlist: A list with details about the model. The user shouldn't ever edit this directly.
ffexp: An ffexp R6 object used to run the experiment and store the results.
eval_func: The function we evaluate.
extract_output_func: A function that takes in the output from 'eval_func' and returns the value we are trying to minimize.
par_all_cts: Are all the parameters continuous?
verbose: How much should be printed? 0 is none, 1 is standard, 2 is more, 5+ is a lot

Active bindings

mod: Gaussian process model used to predict what the output will be.

Methods

Public methods

R6_hype$new()
R6_hype$add_data()
R6_hype$add_X()
R6_hype$add_LHS()
R6_hype$convert_trans_to_raw()
R6_hype$convert_raw_to_trans()
R6_hype$change_par_bounds()
R6_hype$add_EI()
R6_hype$fit_mod()
R6_hype$run_all()
R6_hype$run_EI_for_time()
R6_hype$plot()
R6_hype$pairs()
R6_hype$plotorder()
R6_hype$plotX()
R6_hype$plotXorder()
R6_hype$plotinteractions()
R6_hype$print()
R6_hype$best_params()
R6_hype$update_mod_userspeclist()
R6_hype$clone()

Method `new()`

Create hype R6 object.

Usage

R6_hype$new(
  eval_func,
  ...,
  X0 = NULL,
  Z0 = NULL,
  n_lhs,
  extract_output_func,
  verbose = 1,
  model = "GauPro",
  covtype = "matern5_2",
  nugget.estim = TRUE
)

Arguments

eval_func: The function used to evaluate new points.
...: Hyperparameters to optimize over.
X0: Data frame of initial points to run, or points already evaluated. If already evaluated, give in outputs in "Z0"
Z0: Evaluated outputs at "X0".
n_lhs: The number that should initially be run using a maximin Latin hypercube.
extract_output_func: A function that takes in the output from 'eval_func' and returns the value we are trying to minimize.
verbose: How much should be printed? 0 is none, 1 is standard, 2 is more, 5+ is a lot
model: What package to fit the Gaussian process model with. Either "GauPro" or "DiceKriging"/"DK".
covtype: Covariance/correlation/kernel function for the GP model.
nugget.estim: Should the nugget be estimated when fitting the GP model?

Method `add_data()`

Add data to the experiment results.

Usage

R6_hype$add_data(X, Z)

Arguments

X: Data frame with names matching the input parameters
Z: Output at rows of X matching the experiment output.

Method `add_X()`

Add new inputs to run. This allows the user to specify what they want run next.

Usage

R6_hype$add_X(X)

Arguments

X: Data frame with names matching the input parameters.

Method `add_LHS()`

Add new input points using a maximin Latin hypercube. Latin hypercubes are usually more spacing than randomly picking points.

Usage

R6_hype$add_LHS(n, just_return_df = FALSE)

Arguments

n: Number of points to add.
just_return_df: Instead of adding to experiment, should it just return the new set of values?

Method `convert_trans_to_raw()`

Convert parameters from transformed scale to raw scale.

Usage

R6_hype$convert_trans_to_raw(Xtrans)

Arguments

Xtrans: Parameters on the transformed scale

Method `convert_raw_to_trans()`

Convert parameters from raw scale to transformed scale.

Usage

R6_hype$convert_raw_to_trans(Xraw)

Arguments

Xraw: Parameters on the raw scale

Method `change_par_bounds()`

Change lower/upper bounds of a parameter

Usage

R6_hype$change_par_bounds(parname, lower, upper)

Arguments

parname: Name of the parameter
lower: New lower bound. Leave empty if not changing.
upper: New upper bound. Leave empty if not changing.

Method `add_EI()`

Add new inputs to run using the expected information criteria

Usage

R6_hype$add_EI(
  n,
  covtype = NULL,
  nugget.estim = NULL,
  model = NULL,
  eps,
  just_return = FALSE,
  calculate_at
)

Arguments

n: Number of points to add.
covtype: Covariance function to use for the Gaussian process model.
nugget.estim: Should a nugget be estimated?
model: Which package should be used to fit the model and calculate the EI? Use "DK" for DiceKriging or "GauPro" for GauPro.
eps: Exploration parameter. The minimum amount of improvement you care about.
just_return: Just return the EI info, don't actually add the points to the design.
calculate_at: Calculate the EI at a specific point.

Method `fit_mod()`

Fit model to the data collected so far

Usage

R6_hype$fit_mod(covtype = NULL, nugget.estim = NULL, model = NULL)

Arguments

covtype: Covariance function to use for the Gaussian process model.
nugget.estim: Should a nugget be estimated?
model: Which package should be used to fit the model and calculate the EI? Use "DK" for DiceKriging or "GauPro" for GauPro.

Method `run_all()`

Run all unevaluated input points.

Usage

R6_hype$run_all(...)

Arguments

...: Passed into ‘ffexp$run_all'. Can set ’parallel=TRUE' to evaluate multiple points simultaneously as long as all needed variables have been passed to 'varlist'

Method `run_EI_for_time()`

Add points using the expected information criteria, evaluate them, and repeat until a specified amount of time has passed.

Usage

R6_hype$run_EI_for_time(
  sec,
  batch_size,
  covtype = "matern5_2",
  nugget.estim = TRUE,
  verbose = 0,
  model = "GauPro",
  eps = 0,
  ...
)

Arguments

sec: Number of seconds to run for. It will go over this time limit, finish the current iteration, then stop.
batch_size: Number of points to run at once.
covtype: Covariance function to use for the Gaussian process model.
nugget.estim: Should a nugget be estimated?
verbose: Verbose parameter to pass to ffexp$
model: Which package should be used to fit the model and calculate the EI? Use "DK" for DiceKriging or "GauPro" for GauPro.
eps: Exploration parameter. The minimum amount of improvement you care about.
...: Passed into 'ffexp$run_all'.

Method `plot()`

Make a plot to summarize the experiment.

Usage

R6_hype$plot()

Method `pairs()`

Plot pairs of inputs and output

Usage

R6_hype$pairs()

Method `plotorder()`

Plot the output of the points evaluated in order.

Usage

R6_hype$plotorder()

Method `plotX()`

Plot the output as a function of each input.

Usage

R6_hype$plotX(
  addlines = TRUE,
  addEIlines = TRUE,
  covtype = NULL,
  nugget.estim = NULL,
  model = NULL
)

Arguments

addlines: Should prediction mean and 95% interval be plotted?
addEIlines: Should expected improvement lines be plotted?
covtype: Covariance function to use for the Gaussian process model.
nugget.estim: Should a nugget be estimated?
model: Which package should be used to fit the model and calculate the EI? Use "DK" for DiceKriging or "GauPro" for GauPro.

Method `plotXorder()`

Plot each input in the order they were chosen. Colored by quality.

Usage

R6_hype$plotXorder()

Method `plotinteractions()`

Plot the 2D plots from inputs to the output. All other variables are held at their values for the best input.

Usage

R6_hype$plotinteractions(covtype = "matern5_2", nugget.estim = TRUE)

Arguments

covtype: Covariance function to use for the Gaussian process model.
nugget.estim: Should a nugget be estimated?

Method `print()`

Print details of the object.

Usage

R6_hype$print(...)

Arguments

...: not used

Method `best_params()`

Returns the best parameters evaluated so far.

Usage

R6_hype$best_params()

Method `update_mod_userspeclist()`

Updates the specifications for the GP model.

Usage

R6_hype$update_mod_userspeclist(
  model = NULL,
  covtype = NULL,
  nugget.estim = NULL
)

Arguments

model: What package to fit the Gaussian process model with. Either "GauPro" or "DiceKriging"/"DK".
covtype: Covariance/correlation/kernel function for the GP model.
nugget.estim: Should the nugget be estimated when fitting the GP model?

Method `clone()`

The objects of this class are cloneable with this method.

Usage

R6_hype$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


# Have df output, but only use one value from it
h1 <- hype(
  eval_func = function(a, b) {data.frame(c=a^2+b^2, d=1:2)},
  extract_output_func = function(odf) {odf$c[1]},
  a = par_unif('a', -1, 2),
  b = par_unif('b', -10, 10),
  n_lhs = 10
)
h1$run_all()
h1$add_EI(n = 1)
h1$run_all()
#system.time(h1$run_EI_for_time(sec=3, batch_size = 1))
#system.time(h1$run_EI_for_time(sec=3, batch_size = 3))
h1$plotorder()
h1$plotX()

comparer documentation built on March 7, 2023, 7:49 p.m.

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README.md hype: Hyperparameter Optimization" Introduction to the comparer R package"

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comparer Compare Output and Run Time

R6_hype: Hyperparameter optimization In comparer: Compare Output and Run Time

Hyperparameter optimization

Description

Public fields

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Method add_data()

Usage

Arguments

Method add_X()

Usage

Arguments

Method add_LHS()

Usage

Arguments

Method convert_trans_to_raw()

Usage

Arguments

Method convert_raw_to_trans()

Usage

Arguments

Method change_par_bounds()

Usage

Arguments

Method add_EI()

Usage

Arguments

Method fit_mod()

Usage

Arguments

Method run_all()

Usage

Arguments

Method run_EI_for_time()

Usage

Arguments

Method plot()

Usage

Method pairs()

Usage

Method plotorder()

Usage

Method plotX()

Usage

Arguments

Method plotXorder()

Usage

Method plotinteractions()

Usage

Arguments

Method print()

Usage

Arguments

Method best_params()

Usage

Method update_mod_userspeclist()

Usage

Arguments

Method clone()

Usage

Arguments

Examples

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Compare Output and Run Time

R6_hype: Hyperparameter optimization
In comparer: Compare Output and Run Time

Method `new()`

Method `add_data()`

Method `add_X()`

Method `add_LHS()`

Method `convert_trans_to_raw()`

Method `convert_raw_to_trans()`

Method `change_par_bounds()`

Method `add_EI()`

Method `fit_mod()`

Method `run_all()`

Method `run_EI_for_time()`

Method `plot()`

Method `pairs()`

Method `plotorder()`

Method `plotX()`

Method `plotXorder()`

Method `plotinteractions()`

Method `print()`

Method `best_params()`

Method `update_mod_userspeclist()`

Method `clone()`