https://github.com/dynverse/dynparam
https://github.com/dynverse/dynparam
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/dynverse/dynparam
- Owner: dynverse
- Created: 2018-11-23T12:02:28.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-01-04T15:54:07.000Z (about 4 years ago)
- Last Synced: 2024-09-22T22:38:18.060Z (5 months ago)
- Language: R
- Size: 554 KB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
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README
---
output: github_document
editor_options:
chunk_output_type: console
---
# dynparam
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.
```{r, message = FALSE, error = FALSE, warning = FALSE}
library(tidyverse)
library(dynparam)
set.seed(1)
```
```{r, echo=FALSE}
cat <- function(x) {
base::cat(
"```yaml\n",
x, "\n",
"```",
sep = ""
)
}
```
Define a parameter set as follows:
```{r}
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:
```{r}
get_defaults(parameters)
```
Serialise a parameter set from/to json/yaml with the `as.list()` and `as_parameter_set()` functions.
```{r}
li <- as.list(parameters)
pa <- as_parameter_set(li)
```
Sample a parameter set using with `sip()`:
```{r}
sip(pa, n = 3)
```
### Large parameter set
```{r}
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:
```{r comment = "", results = "asis"}
cat(yaml::as.yaml(as.list(parameters)))
```
Generate a random parameter set:
```{r}
sip(parameters, n = 2)
```
Convert paramhelper object:
```{r}
as_paramhelper(parameters)
```
## Latest changes
Check out `news(package = "dynparam")` or [NEWS.md](inst/NEWS.md) for a full list of changes.
```{r news, echo=FALSE, results="asis"}
dynutils::update_news()
base::cat(dynutils::recent_news())
```
## Dynverse dependencies
