automl_train: automl_train

Description

The multi deep neural network automatic train function (several deep neural networks are trained with automatic hyperparameters tuning, best model is kept) This function launches the automl_train_manual function for each particle at each converging step

Usage

automl_train(Xref, Yref, autopar = list(), hpar = list())

Arguments

Xref

inputs matrix or data.frame (containing numerical values only)

Yref

target matrix or data.frame (containing numerical values only)

autopar

list of parameters for hyperparameters optimization, see autopar section Not mandatory (the list is preset and all arguments are initialized with default value) but it is advisable to adjust some important arguments for performance reasons (including processing time)

hpar

list of parameters and hyperparameters for Deep Neural Network, see hpar section Not mandatory (the list is preset and all arguments are initialized with default value) but it is advisable to adjust some important arguments for performance reasons (including processing time)

Examples

Run this code

# NOT RUN {
##REGRESSION (predict Sepal.Length given other Iris parameters)
data(iris)
xmat <- cbind(iris[,2:4], as.numeric(iris$Species))
ymat <- iris[,1]
amlmodel <- automl_train(Xref = xmat, Yref = ymat)
# }
# NOT RUN {
##CLASSIFICATION (predict Species given other Iris parameters)
data(iris)
xmat = iris[,1:4]
lab2pred <- levels(iris$Species)
lghlab <- length(lab2pred)
iris$Species <- as.numeric(iris$Species)
ymat <- matrix(seq(from = 1, to = lghlab, by = 1), nrow(xmat), lghlab, byrow = TRUE)
ymat <- (ymat == as.numeric(iris$Species)) + 0
#with gradient descent and random hyperparameters sets
amlmodel <- automl_train(Xref = xmat, Yref = ymat,
                          autopar = list(numiterations = 1, psopartpopsize = 1, seed = 11),
                          hpar = list(numiterations = 10))
# }

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