sits_combine_predictions {sits}

Estimate ensemble prediction based on list of probs cubes

Description

Calculate an ensemble predictor based a list of probability cubes. The function combines the output of two or more classifier to derive a value which is based on weights assigned to each model. The supported types of ensemble predictors are 'average' and 'uncertainty'.

Usage

sits_combine_predictions(
  cubes,
  type = "average",
  ...,
  memsize = 8L,
  multicores = 2L,
  output_dir,
  version = "v1"
)

## S3 method for class 'average'
sits_combine_predictions(
  cubes,
  type = "average",
  ...,
  weights = NULL,
  memsize = 8L,
  multicores = 2L,
  output_dir,
  version = "v1"
)

## S3 method for class 'uncertainty'
sits_combine_predictions(
  cubes,
  type = "uncertainty",
  ...,
  uncert_cubes,
  memsize = 8L,
  multicores = 2L,
  output_dir,
  version = "v1"
)

## Default S3 method:
sits_combine_predictions(cubes, type, ...)

Arguments

cubes

List of probability data cubes (class "probs_cube")

type

Method to measure uncertainty. One of "average" or "uncertainty"

...

Parameters for specific functions.

memsize

Memory available for classification in GB (integer, min = 1, max = 16384).

multicores

Number of cores to be used for classification (integer, min = 1, max = 2048).

output_dir

Valid directory for output file. (character vector of length 1).

version

Version of the output (character vector of length 1).

weights

Weights for averaging (numeric vector).

uncert_cubes

Uncertainty cubes to be used as local weights when type = "uncertainty" is selected (list of tibbles with class "uncertainty_cube")

Value

A combined probability cube (tibble of class "probs_cube").

Author(s)

Gilberto Camara, [e-mail]

Rolf Simoes, [e-mail]

Examples

if (sits_run_examples()) {
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6",
        data_dir = data_dir
    )
    # create a random forest model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # classify a data cube using rfor model
    probs_rfor_cube <- sits_classify(
        data = cube, ml_model = rfor_model, output_dir = tempdir(),
        version = "rfor"
    )
    # create an XGBoost model
    svm_model <- sits_train(samples_modis_ndvi, sits_svm())
    # classify a data cube using SVM model
    probs_svm_cube <- sits_classify(
        data = cube, ml_model = svm_model, output_dir = tempdir(),
        version = "svm"
    )
    # create a list of predictions to be combined
    pred_cubes <- list(probs_rfor_cube, probs_svm_cube)
    # combine predictions
    comb_probs_cube <- sits_combine_predictions(
        pred_cubes,
        output_dir = tempdir()
    )
    # plot the resulting combined prediction cube
    plot(comb_probs_cube)
}