lantern_logistic_reg: Fit a single layer neural network
In tidymodels/lantern: High-Level Modeling Functions with 'torch'

Description Usage Arguments Details Value Examples

View source: R/lantern_logistic_reg-fit.R

lantern_logistic_reg() fits a model.

lantern_logistic_reg(x, ...)

## Default S3 method:
lantern_logistic_reg(x, ...)

## S3 method for class 'data.frame'
lantern_logistic_reg(
  x,
  y,
  epochs = 100L,
  penalty = 0,
  validation = 0.1,
  learn_rate = 0.01,
  momentum = 0,
  batch_size = NULL,
  conv_crit = -Inf,
  verbose = FALSE,
  ...
)

## S3 method for class 'matrix'
lantern_logistic_reg(
  x,
  y,
  epochs = 100L,
  penalty = 0,
  validation = 0.1,
  learn_rate = 0.01,
  momentum = 0,
  batch_size = NULL,
  conv_crit = -Inf,
  verbose = FALSE,
  ...
)

## S3 method for class 'formula'
lantern_logistic_reg(
  formula,
  data,
  epochs = 100L,
  penalty = 0,
  validation = 0.1,
  learn_rate = 0.01,
  momentum = 0,
  batch_size = NULL,
  conv_crit = -Inf,
  verbose = FALSE,
  ...
)

## S3 method for class 'recipe'
lantern_logistic_reg(
  x,
  data,
  epochs = 100L,
  penalty = 0,
  validation = 0.1,
  learn_rate = 0.01,
  momentum = 0,
  batch_size = NULL,
  conv_crit = -Inf,
  verbose = FALSE,
  ...
)

`x`	Depending on the context: A data frame of predictors. A matrix of predictors. A recipe specifying a set of preprocessing steps created from `recipes::recipe()`. The predictor data should be standardized (e.g. centered or scaled).
`...`	Not currently used, but required for extensibility.
`y`	When `x` is a data frame or matrix, `y` is the outcome specified as: A data frame with 1 numeric column. A matrix with 1 numeric column. A numeric vector.
`epochs`	An integer for the number of epochs of training.
`penalty`	The amount of weight decay (i.e., L2 regularization).
`validation`	The proportion of the data randomly assigned to a validation set.
`learn_rate`	A positive number (usually less than 0.1).
`momentum`	A positive number on `[0, 1]` for the momentum parameter in gradient decent.
`batch_size`	An integer for the number of training set points in each batch.
`conv_crit`	A non-negative number for convergence.
`verbose`	A logical that prints out the iteration history.
`formula`	A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.
`data`	When a recipe or formula is used, `data` is specified as: A data frame containing both the predictors and the outcome.

Despite its name, this function can be used with three or more classes (e.g., multinomial regression).

The predictors data should all be numeric and encoded in the same units (e.g. standardized to the same range or distribution). If there are factor predictors, use a recipe or formula to create indicator variables (or some other method) to make them numeric.

If conv_crit is used, it stops training when the difference in the loss function is below conv_crit or if it gets worse. The default trains the model over the specified number of epochs.

A lantern_logistic_reg object with elements:

models: a list object of serialized models for each epoch.
loss: A vector of loss values (MSE for regression, negative log- likelihood for classification) at each epoch.
dim: A list of data dimensions.
y_stats: A list of summary statistics for numeric outcomes.
parameters: A list of some tuning parameter values.
blueprint: The hardhat blueprint data.

if (torch::torch_is_installed()) {

 ## -----------------------------------------------------------------------------
 # increase # epochs to get better results

 data(cells, package = "modeldata")

 cells$case <- NULL

 set.seed(122)
 in_train <- sample(1:nrow(cells), 1000)
 cells_train <- cells[ in_train,]
 cells_test  <- cells[-in_train,]

 # Using matrices
 set.seed(1)
 lantern_logistic_reg(x = as.matrix(cells_train[, c("fiber_width_ch_1", "width_ch_1")]),
                      y = cells_train$class,
                      penalty = 0.10, epochs = 20L, batch_size = 32)

 # Using recipe
 library(recipes)

 cells_rec <-
  recipe(class ~ ., data = cells_train) %>%
  # Transform some highly skewed predictors
  step_YeoJohnson(all_predictors()) %>%
  step_normalize(all_predictors())

 set.seed(2)
 fit <- lantern_logistic_reg(cells_rec, data = cells_train,
                             penalty = .01, epochs = 100L, batch_size = 32)
 fit

 autoplot(fit)

 library(yardstick)
 predict(fit, cells_test, type = "prob") %>%
  bind_cols(cells_test) %>%
  roc_auc(class, .pred_PS)

 # ------------------------------------------------------------------------------
 # multinomial regression

 data(penguins, package = "modeldata")

 penguins <- penguins %>% na.omit()

 set.seed(122)
 in_train <- sample(1:nrow(penguins), 200)
 penguins_train <- penguins[ in_train,]
 penguins_test  <- penguins[-in_train,]

 rec <- recipe(island ~ ., data = penguins_train) %>%
  step_dummy(species, sex) %>%
  step_normalize(all_predictors())

 set.seed(3)
 fit <- lantern_logistic_reg(rec, data = penguins_train,
                             epochs = 200L, batch_size = 32)
 fit

 predict(fit, penguins_test) %>%
  bind_cols(penguins_test) %>%
  conf_mat(island, .pred_class)
}