Provides tools for describing parameters of algorithms in an abstract
way. Description can include an id, a description, a domain (range or
list of values), and a default value. ‘dynparam’ can also convert
parameter sets to a ParamHelpers format, in order to be able to use
dynparam in conjunction with mlrMBO. Check ?dynparam for an
overview of all functionality provided by dynparam.
Examples
The main goal of dynparam is to be able to describe a set of
parameters, be able to serialise the parameter sets, and also sample
random settings from the parameter set.
library(tidyverse) library(dynparam) set.seed(1)
Define a parameter set as follows:
parameters <- parameter_set( integer_parameter( id = "num_iter", default = 100L, distribution = expuniform_distribution(lower = 1L, upper = 10000L), description = "Number of iterations" ), subset_parameter( id = "dimreds", default = c("pca", "mds"), values = c("pca", "mds", "tsne", "umap", "ica"), description = "Which dimensionality reduction methods to apply (can be multiple)" ), integer_range_parameter( id = "ks", default = c(3L, 15L), lower_distribution = uniform_distribution(1L, 5L), upper_distribution = uniform_distribution(10L, 20L), description = "The numbers of clusters to be evaluated" ) )
You can retrieve the default parameters as follows:
get_defaults(parameters)## $num_iter
## [1] 100
##
## $dimreds
## [1] "pca" "mds"
##
## $ks
## [1] 3 15
Serialise a parameter set from/to json/yaml with the as.list() and
as_parameter_set() functions.
li <- as.list(parameters) pa <- as_parameter_set(li)
Sample a parameter set using with sip():
sip(pa, n = 3)
## Loading required namespace: ParamHelpers
## Loading required namespace: lhs
## # A tibble: 3 x 4
## num_iter dimreds ks .object_class
## <int> <list> <list> <list>
## 1 2 <chr [2]> <dbl [2]> <chr [1]>
## 2 2003 <chr [2]> <dbl [2]> <chr [1]>
## 3 5 <chr [3]> <dbl [2]> <chr [1]>
Large parameter set
parameters <- parameter_set( integer_parameter( id = "num_iter", default = 100L, distribution = expuniform_distribution(lower = 1L, upper = 10000L), description = "Number of iterations" ), numeric_parameter( id = "delta", default = c(4.5, 2.4, 1.9), distribution = normal_distribution(mean = 5, sd = 1), description = "Multiplying factors" ), character_parameter( id = "method", default = "kendall", values = c("kendall", "spearman", "pearson"), description = "Correlation method" ), logical_parameter( id = "inverse", default = TRUE, description = "Inversion parameter" ), subset_parameter( id = "dimreds", default = c("pca", "mds"), values = c("pca", "mds", "tsne", "umap", "ica"), description = "Which dimensionality reduction methods to apply (can be multiple)" ), integer_range_parameter( id = "ks", default = c(3L, 15L), lower_distribution = uniform_distribution(1L, 5L), upper_distribution = uniform_distribution(10L, 20L), description = "The numbers of clusters to be evaluated." ), numeric_range_parameter( id = "quantiles", default = c(0.15, 0.90), lower_distribution = uniform_distribution(0, .4), upper_distribution = uniform_distribution(.6, 1), description = "Quantile cutoff range" ), forbidden = "inverse == (method == 'kendall')" )
As yaml:
cat(yaml::as.yaml(as.list(parameters)))
- id: num_iter default: 100 description: Number of iterations tuneable: yes distribution: lower: 1 upper: 10000 type: expuniform type: integer - id: delta default: - 4.5 - 2.4 - 1.9 description: Multiplying factors tuneable: yes distribution: lower: -.inf upper: .inf mean: 5.0 sd: 1.0 type: normal type: numeric - id: method default: kendall description: Correlation method tuneable: yes values: - kendall - spearman - pearson type: character - id: inverse default: yes description: Inversion parameter tuneable: yes type: logical - id: dimreds default: - pca - mds description: Which dimensionality reduction methods to apply (can be multiple) tuneable: yes values: - pca - mds - tsne - umap - ica type: subset - id: ks default: - 3 - 15 description: The numbers of clusters to be evaluated. tuneable: yes lower_distribution: lower: 1 upper: 5 type: uniform upper_distribution: lower: 10 upper: 20 type: uniform type: integer_range - id: quantiles default: - 0.15 - 0.9 description: Quantile cutoff range tuneable: yes lower_distribution: lower: 0.0 upper: 0.4 type: uniform upper_distribution: lower: 0.6 upper: 1.0 type: uniform type: numeric_range - forbidden: inverse == (method == 'kendall')
Generate a random parameter set:
sip(parameters, n = 2)
## # A tibble: 2 x 8
## num_iter delta method inverse dimreds ks quantiles .object_class
## <int> <list> <chr> <lgl> <list> <list> <list> <list>
## 1 1823 <dbl [3]> kendall FALSE <chr [3]> <dbl [2]> <dbl [2]> <chr [1]>
## 2 125 <dbl [3]> pearson TRUE <chr [2]> <dbl [2]> <dbl [2]> <chr [1]>
Convert paramhelper object:
as_paramhelper(parameters)## Type len Def Constr Req
## num_iter numeric - 0.5 0 to 1 -
## delta numericvector 3 0.309,0.0046... 0 to 1 -
## method discrete - kendall kendall,spearman,pearson -
## inverse logical - TRUE - -
## dimreds integervector 5 1,1,0,0,0 0 to 1 -
## ks numericvector 2 0.5,0.5 0 to 1 -
## quantiles numericvector 2 0.375,0.75 0 to 1 -
## Tunable Trafo
## num_iter TRUE Y
## delta TRUE Y
## method TRUE -
## inverse TRUE -
## dimreds TRUE Y
## ks TRUE Y
## quantiles TRUE Y
## Forbidden region specified.
Latest changes
Check out news(package = "dynparam") or NEWS.md for a full
list of changes.
Recent changes in dynparam 1.0.2 (04-01-2021)
- MINOR CHANGES: Set
check.environmenttoFALSEin unit tests to fix breaking changes in R devel.
Recent changes in dynparam 1.0.1 (24-06-2020)
- MINOR CHANGES: Move testthat to Suggests.
- MINOR CHANGES: Use
expect_equal(., ., tolerance = .)instead ofexpect_lte(abs(. - .), .)in tests.
Dynverse dependencies
