kerasformula (version 0.1.0)

kms: kms

Description

A regression-style function call for keras_model_sequential() which uses formulas and sparse matrices. A sequential model is a linear stack of layers.

Usage

kms(input_formula, data, keras_model_seq = NULL, layers = list(units =
  c(256, 128, NA), activation = c("relu", "relu", "softmax"), dropout = c(0.4,
  0.3, NA)), pTraining = 0.8, seed = NULL, validation_split = 0.2,
  Nepochs = 25, batch_size = 32, loss = NULL, metrics = c("accuracy"),
  optimizer = "optimizer_rmsprop", ...)

Arguments

input_formula

an object of class "formula" (or one coerceable to a formula): a symbolic description of the keras inputs. The outcome, y, is assumed to be categorical, e.g. "stars ~ mentions.tasty + mentions.fun".

data

a data.frame.

keras_model_seq

A compiled Keras sequential model. If non-NULL (NULL is the default), then bypasses the following `kms` parameters: layers, loss, metrics, and optimizer.

layers

a list that creates a dense Keras model. Contains the number of units, activation type, and dropout rate. Example with three layers: layers = list(units = c(256, 128, NA), activation = c("relu", "relu", "softmax"), dropout = c(0.4, 0.3, NA)). If the final element of units is NA (default), set to the number of unique elements in y. See ?layer_dense or ?layer_dropout.

pTraining

Proportion of the data to be used for training the model; 0 < pTraining < 1. By default, pTraining == 0.8. Other observations used only postestimation (e.g., for confusion matrix).

seed

seed to passed to set.seed for partitioning data. If NULL (default), automatically generated.

validation_split

Portion of data to be used for validating each epoch (i.e., portion of pTraining). To be passed to keras::fit. Default == 0.2.

Nepochs

Number of epochs. To be passed to keras::fit. Default == 25.

batch_size

To be passed to keras::fit. Default == 32.

loss

To be passed to keras::compile. Defaults to "binary_crossentropy" or "categorical_crossentropy" based on the number of distinct elements of y.

metrics

To be passed to keras::compile. Default == c("accuracy").

optimizer

To be passed to keras::compile. Default == "optimizer_rmsprop".

...

Additional parameters to be passsed to Matrix::sparse.model.matrix.

Value

kms_fit object. A list containing model, predictions, evaluations, as well as other details like how the data were split into testing and training.

Examples

# NOT RUN {
if(is_keras_available()){

 mtcars$make <- unlist(lapply(strsplit(rownames(mtcars), " "), function(tokens) tokens[1]))
 company <- kms(make ~ ., mtcars, Nepochs = 10)
 # out of sample accuracy
 pCorrect <- mean(company$y_test == company$predictions)
 pCorrect
 company$confusion
 # plot(history$company) # helps pick Nepochs
 company <- kms(make ~ ., mtcars, Nepochs = 3, seed = 2018,
               layers = list(units = c(11, 9, NA), activation = c("relu", "relu", "softmax"),
               dropout = c(0.4, 0.3, NA)))
}else{
   cat("Please run install_keras() before using kms(). ?install_keras for options and details." )
}
 
# }