tests/testthat/test_e2e_seq_regression.R

In kerasnip: A Bridge Between 'keras' and 'tidymodels'

test_that("E2E: Regression spec generation, fitting, and prediction works", {
  skip_if_no_keras()

  input_block_reg <- function(model, input_shape) {
    keras3::keras_model_sequential(input_shape = input_shape)
  }
  dense_block_reg <- function(model, units = 16, dropout = 0.1) {
    model |>
      keras3::layer_dense(units = units, activation = "relu") |>
      keras3::layer_dropout(rate = dropout)
  }
  output_block_reg <- function(model) {
    model |> keras3::layer_dense(units = 1)
  }

  model_name <- "e2e_mlp_reg"
  on.exit(suppressMessages(remove_keras_spec(model_name)), add = TRUE)

  create_keras_sequential_spec(
    model_name = model_name,
    layer_blocks = list(
      input = input_block_reg,
      dense = dense_block_reg,
      output = output_block_reg
    ),
    mode = "regression"
  )

  spec <- e2e_mlp_reg(
    num_dense = 2,
    dense_units = 8,
    fit_epochs = 2,
    learn_rate = 0.01
  ) |>
    set_engine("keras")

  data <- mtcars
  rec <- recipe(mpg ~ ., data = data)
  wf <- workflow(rec, spec)

  expect_no_error(
    fit_obj <- fit(wf, data = data)
  )
  expect_s3_class(fit_obj, "workflow")

  preds <- predict(fit_obj, new_data = data[1:5, ])
  expect_s3_class(preds, "tbl_df")
  expect_equal(names(preds), ".pred")
  expect_equal(nrow(preds), 5)
  expect_true(is.numeric(preds$.pred))
})

test_that("E2E: Sequential regression works with named predictors in formula", {
  skip_if_no_keras()

  input_block_reg <- function(model, input_shape) {
    keras3::keras_model_sequential(input_shape = input_shape)
  }
  dense_block_reg <- function(model, units = 16, dropout = 0.1) {
    model |>
      keras3::layer_dense(units = units, activation = "relu") |>
      keras3::layer_dropout(rate = dropout)
  }
  output_block_reg <- function(model) {
    model |> keras3::layer_dense(units = 1)
  }

  model_name <- "e2e_mlp_reg_named"
  on.exit(suppressMessages(remove_keras_spec(model_name)), add = TRUE)

  create_keras_sequential_spec(
    model_name = model_name,
    layer_blocks = list(
      input = input_block_reg,
      dense = dense_block_reg,
      output = output_block_reg
    ),
    mode = "regression"
  )

  spec <- e2e_mlp_reg_named(
    fit_epochs = 1
  ) |>
    set_engine("keras")

  data <- mtcars
  # Use named predictors to cover the x <- data[, x_names, drop = FALSE] line
  expect_no_error(
    fit_obj <- fit(spec, mpg ~ cyl + disp, data = data)
  )
  expect_s3_class(fit_obj, "model_fit")
})