sits (version 1.5.3)

sits_tuning_hparams: Tuning machine learning models hyper-parameters

Description

This function allow user building the hyper-parameters space used by sits_tuning() function search randomly the best parameter combination.

Users should pass the possible values for hyper-parameters as constants or by calling the following random functions:

• uniform(min = 0, max = 1, n = 1): returns random numbers from a uniform distribution with parameters min and max.

• choice(..., replace = TRUE, n = 1): returns random objects passed to ... with replacement or not (parameter replace).

• randint(min, max, n = 1): returns random integers from a uniform distribution with parameters min and max.

• normal(mean = 0, sd = 1, n = 1): returns random numbers from a normal distribution with parameters min and max.

• lognormal(meanlog = 0, sdlog = 1, n = 1): returns random numbers from a lognormal distribution with parameters min and max.

• loguniform(minlog = 0, maxlog = 1, n = 1): returns random numbers from a loguniform distribution with parameters min and max.

• beta(shape1, shape2, n = 1): returns random numbers from a beta distribution with parameters min and max.

These functions accepts n parameter to indicate how many values should be returned.

Usage

sits_tuning_hparams(...)

Value

A list containing the hyper-parameter space to be passed to

sits_tuning()'s params parameter.

Arguments

...

Used to prepare hyper-parameter space

Examples

if (sits_run_examples()) {
    # find best learning rate parameters for TempCNN
    tuned <- sits_tuning(
        samples_modis_ndvi,
        ml_method = sits_tempcnn(),
        params = sits_tuning_hparams(
            optimizer = choice(
                torch::optim_adamw,
                torch::optim_adagrad
            ),
            opt_hparams = list(
                lr = loguniform(10^-2, 10^-4),
                weight_decay = loguniform(10^-2, 10^-8)
            )
        ),
        trials = 20,
        multicores = 2,
        progress = FALSE
    )
}