bHIVE classification model for caret

A custom model definition that lets caret::train() tune, resample and evaluate bHIVE classifiers.

Usage

bHIVEmodel

Format

A list with the components caret expects of a custom model.

Value

A list defining a custom caret model, for use as caret::train(method = bHIVEmodel).

Details

Pass bHIVEmodel as the method of caret::train(). Training fits bHIVE with the tuned nAntibodies, beta and epsilon. Further arguments given to caret::train() (for example maxIter, scale or immunology modules) are passed on to bHIVE(). Progress output is off unless verbose = TRUE is passed. Prediction labels each new observation with the class of its best-matching antibody, using the fitted AINet model, so new data gets the same scaling and kernel as the training data.

caret requires column names on x.

Components

label

"Artificial Immune Network (bHIVE)".

library

"bHIVE".

type

"Classification".

parameters

The tunable parameters: nAntibodies (initial antibody count), beta (clone multiplier) and epsilon (suppression threshold).

grid

function(x, y, len, search). A default tuning grid, regular (search = "grid") or random.

fit

function(x, y, wts, param, lev, last, classProbs, ...). Fits bHIVE() and returns its result.

predict

function(modelFit, newdata, submodels). Returns a factor of predicted classes.

prob

NULL. Class probabilities are not implemented, so caret::train() warns and continues if classProbs = TRUE.

See also

bHIVE, honeycombHIVEmodel, train

Examples

bHIVEmodel$label
#> [1] "Artificial Immune Network (bHIVE)"
bHIVEmodel$parameters
#>     parameter   class                label
#> 1 nAntibodies numeric Number of Antibodies
#> 2        beta numeric     Clone Multiplier
#> 3     epsilon numeric    Epsilon Threshold

if (requireNamespace("caret", quietly = TRUE)) {
    X <- as.matrix(iris[, 1:4])
    y <- iris$Species
    set.seed(42)
    fit <- caret::train(
        x = X, y = y, method = bHIVEmodel,
        trControl = caret::trainControl(method = "cv", number = 3),
        tuneGrid = data.frame(nAntibodies = 40, beta = 5, epsilon = 0.05),
        maxIter = 10
    )
    fit
    table(Predicted = predict(fit, X), Actual = y)
}
#> Loading required package: ggplot2
#> Loading required package: lattice
#> 
#> Attaching package: ‘caret’
#> The following object is masked from ‘package:generics’:
#> 
#>     train
#>             Actual
#> Predicted    setosa versicolor virginica
#>   setosa         50          0         0
#>   versicolor      0         43         2
#>   virginica       0          7        48