lantern_logistic_reg: Fit a single layer neural network

Description Usage Arguments Details Value Examples

View source: R/lantern_logistic_reg-fit.R

Description

lantern_logistic_reg() fits a model.

Usage

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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,
  ...
)

Arguments

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.

Details

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.

Value

A lantern_logistic_reg object with elements:

Examples

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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)
}

tidymodels/lantern documentation built on March 8, 2021, 8:53 a.m.