Skip to contents

Divergence-based classifier as a 'parsnip' model

Source: R/parsnip.R
divergence_classifier.Rd

divergence_classifier() defines the divergence-based classifier of Ferreira, Coelho and Nascimento (2021) as a 'parsnip' model specification, so it can be used with 'workflows', 'recipes', 'rsample', 'tune' and 'yardstick'. The engine selects the family of features: "gaussian" (multivariate normal intensity features, the default) or "wishart" (complex Wishart PolSAR matrix features). The predictors must be in the format of sar_features(); extra columns such as row/col are ignored by the engine (it is good practice to give them an "id" role in a recipe).

Usage

divergence_classifier(
  mode = "classification",
  divergence = NULL,
  order = NULL,
  kind = NULL,
  engine = "gaussian"
)

# S3 method for class 'divergence_classifier'
update(
  object,
  parameters = NULL,
  divergence = NULL,
  order = NULL,
  kind = NULL,
  fresh = FALSE,
  ...
)

Arguments

mode

A single character string for the type of model. The only possible value is "classification".

divergence

Divergence: "renyi" (default), "kl", "jeffreys" or "bhattacharyya".

order

Order of the Rényi divergence, in (0, 1). Tunable with renyi_order().

kind

Form of the Rényi divergence (1 to 4); see divergences.

engine

"gaussian" or "wishart".

object

A divergence_classifier model specification.

parameters

A 1-row tibble or named list with the main parameters.

fresh

Replace the arguments instead of updating them?

...

Not used.

Value

A model specification object with classes divergence_classifier and model_spec.

See also

DivergenceClassifier, sar_features()

Examples

spec <- divergence_classifier(divergence = "renyi", order = 0.5) |>
  parsnip::set_engine("wishart")
spec
#> Divergence Classifier Model Specification (classification)
#> 
#> Main Arguments:
#>   divergence = renyi
#>   order = 0.5
#> 
#> Computational engine: wishart 
#> 

feats <- sar_features(SARImage$new(SanFrancisco150)$crop(1:60, 1:60), window = 5)
feats$class <- factor(as.vector(SanFrancisco150_gt[1:60, 1:60]))
fit <- parsnip::fit(spec, class ~ ., data = feats)
head(predict(fit, feats))
#> # A tibble: 6 × 1
#>   .pred_class
#>   <fct>      
#> 1 1          
#> 2 1          
#> 3 1          
#> 4 1          
#> 5 1          
#> 6 1          
head(predict(fit, feats, type = "prob"))
#> # A tibble: 6 × 1
#>   .pred_1
#>     <dbl>
#> 1       1
#> 2       1
#> 3       1
#> 4       1
#> 5       1
#> 6       1