hpc <- hpc_data[1:150, c(2:5, 8)]


num_pred <- names(hpc)[1:4]

hpc_keras <-
  mlp(mode = "classification", hidden_units = 2, epochs = 10) %>%
  set_engine("keras", verbose = 0)

nn_dat <- read.csv("nnet_test.txt")

# ------------------------------------------------------------------------------

test_that('keras execution, classification', {
  skip_on_cran()
  skip_if_not_installed("keras")
  skip_if(!is_tf_ok())

  expect_no_condition(
    res <- parsnip::fit(
      hpc_keras,
      class ~ compounds + input_fields,
      data = hpc,
      control = ctrl
    )
  )


  expect_false(has_multi_predict(res))
  expect_equal(multi_predict_args(res), NA_character_)

  keras::backend()$clear_session()

  expect_no_condition(
    res <- parsnip::fit_xy(
      hpc_keras,
      x = hpc[, num_pred],
      y = hpc$class,
      control = ctrl
    )
  )

  keras::backend()$clear_session()

  expect_snapshot(
    error = TRUE,
    res <- parsnip::fit(
      hpc_keras,
      class ~ novar,
      data = hpc,
      control = ctrl
    )
  )
})


test_that('keras classification prediction', {
  skip_on_cran()
  skip_if_not_installed("keras")
  skip_if(!is_tf_ok())
  library(keras)

  xy_fit <- parsnip::fit_xy(
    hpc_keras,
    x = hpc[, num_pred],
    y = hpc$class,
    control = ctrl
  )

  xy_pred <- predict(extract_fit_engine(xy_fit), x = as.matrix(hpc[1:8, num_pred]))
  if (tensorflow::tf_version() <= package_version("2.0.0")) {
    # -1 to assign with keras' zero indexing
    xy_pred <- apply(xy_pred, 1, which.max) - 1
  } else {
    xy_pred <- xy_pred %>% keras::k_argmax() %>% as.integer()
  }

  xy_pred <- factor(levels(hpc$class)[xy_pred + 1], levels = levels(hpc$class))
  expect_equal(xy_pred, predict(xy_fit, new_data = hpc[1:8, num_pred], type = "class")[[".pred_class"]])

  keras::backend()$clear_session()

  form_fit <- parsnip::fit(
    hpc_keras,
    class ~ .,
    data = hpc,
    control = ctrl
  )


  form_pred <- predict(extract_fit_engine(form_fit), x = as.matrix(hpc[1:8, num_pred]))
  if (tensorflow::tf_version() <= package_version("2.0.0")) {
    # -1 to assign with keras' zero indexing
    form_pred <- apply(form_pred, 1, which.max) - 1
  } else {
    form_pred <- form_pred %>% keras::k_argmax() %>% as.integer()
  }

  form_pred <- factor(levels(hpc$class)[form_pred + 1], levels = levels(hpc$class))
  expect_equal(form_pred, predict(form_fit, new_data = hpc[1:8, num_pred], type = "class")[[".pred_class"]])

  keras::backend()$clear_session()
})


test_that('keras classification probabilities', {
  skip_on_cran()
  skip_if_not_installed("keras")
  skip_if(!is_tf_ok())

  xy_fit <- parsnip::fit_xy(
    hpc_keras,
    x = hpc[, num_pred],
    y = hpc$class,
    control = ctrl
  )

  xy_pred <- predict(extract_fit_engine(xy_fit), x = as.matrix(hpc[1:8, num_pred]))
  colnames(xy_pred) <- paste0(".pred_", levels(hpc$class))
  xy_pred <- as_tibble(xy_pred)
  expect_equal(xy_pred, predict(xy_fit, new_data = hpc[1:8, num_pred], type = "prob"))

  keras::backend()$clear_session()

  form_fit <- parsnip::fit(
    hpc_keras,
    class ~ .,
    data = hpc,
    control = ctrl
  )

  form_pred <- predict(extract_fit_engine(form_fit), x = as.matrix(hpc[1:8, num_pred]))
  colnames(form_pred) <- paste0(".pred_", levels(hpc$class))
  form_pred <- as_tibble(form_pred)
  expect_equal(form_pred, predict(form_fit, new_data = hpc[1:8, num_pred], type = "prob"))

  keras::backend()$clear_session()
})


# ------------------------------------------------------------------------------

mtcars <- as.data.frame(scale(mtcars))

num_pred <- names(mtcars)[3:6]

car_basic <- mlp(mode = "regression", epochs = 10) %>%
  set_engine("keras", verbose = 0)

bad_keras_reg <-
  mlp(mode = "regression") %>%
  set_engine("keras", min.node.size = -10, verbose = 0)

# ------------------------------------------------------------------------------

test_that('keras execution, regression', {
  skip_on_cran()
  skip_if_not_installed("keras")
  skip_if(!is_tf_ok())

  expect_no_condition(
    res <- parsnip::fit(
      car_basic,
      mpg ~ .,
      data = mtcars,
      control = ctrl
    )
  )

  keras::backend()$clear_session()

  expect_no_condition(
    res <- parsnip::fit_xy(
      car_basic,
      x = mtcars[, num_pred],
      y = mtcars$mpg,
      control = ctrl
    )
  )
})

test_that('keras regression prediction', {
  skip_on_cran()
  skip_if_not_installed("keras")
  skip_if(!is_tf_ok())

  xy_fit <- parsnip::fit_xy(
    mlp(mode = "regression", hidden_units = 2, epochs = 500, penalty = .1) %>%
      set_engine("keras", verbose = 0),
    x = mtcars[, c("cyl", "disp")],
    y = mtcars$mpg,
    control = ctrl
  )

  xy_pred <- predict(extract_fit_engine(xy_fit), x = as.matrix(mtcars[1:8, c("cyl", "disp")]))[,1]
  expect_equal(xy_pred, predict(xy_fit, new_data = mtcars[1:8, c("cyl", "disp")])[[".pred"]])

  keras::backend()$clear_session()

  form_fit <- parsnip::fit(
    car_basic,
    mpg ~ .,
    data = mtcars[, c("cyl", "disp", "mpg")],
    control = ctrl
  )

  form_pred <- predict(extract_fit_engine(form_fit), x = as.matrix(mtcars[1:8, c("cyl", "disp")]))[,1]
  expect_equal(form_pred, predict(form_fit, new_data = mtcars[1:8, c("cyl", "disp")])[[".pred"]])

  keras::backend()$clear_session()
})

# ------------------------------------------------------------------------------

test_that('multivariate nnet formula', {
  skip_on_cran()
  skip_if_not_installed("keras")
  skip_if(!is_tf_ok())

  nnet_form <-
    mlp(mode = "regression", hidden_units = 3, penalty = 0.01)  %>%
    set_engine("keras", verbose = 0) %>%
    parsnip::fit(
      cbind(V1, V2, V3) ~ .,
      data = nn_dat[-(1:5),]
    )
  expect_equal(length(unlist(keras::get_weights(extract_fit_engine(nnet_form)))), 24)

  nnet_form_pred <- predict(nnet_form, new_data = nn_dat[1:5, -(1:3)])
  expect_equal(names(nnet_form_pred), paste0(".pred_", c("V1", "V2", "V3")))

  keras::backend()$clear_session()

  nnet_xy <-
    mlp(mode = "regression", hidden_units = 3, penalty = 0.01)  %>%
    set_engine("keras", verbose = 0) %>%
    parsnip::fit_xy(
      x = nn_dat[-(1:5), -(1:3)],
      y = nn_dat[-(1:5),   1:3 ]
    )
  expect_equal(length(unlist(keras::get_weights(extract_fit_engine(nnet_xy)))), 24)
  nnet_form_xy <- predict(nnet_xy, new_data = nn_dat[1:5, -(1:3)])
  expect_equal(names(nnet_form_pred), paste0(".pred_", c("V1", "V2", "V3")))


  keras::backend()$clear_session()
})

# ------------------------------------------------------------------------------

test_that('all keras activation functions', {
  skip_on_cran()
  skip_if_not_installed("keras")
  skip_if_not_installed("modeldata")
  skip_if_not_installed("dials", minimum_version = "1.3.0.9000")
  skip_if(!is_tf_ok())

  act <- parsnip:::keras_activations()

  test_act <- function(fn) {
    set.seed(1)
    try(
      mlp(mode = "classification", hidden_units = 2, penalty = 0.01, epochs = 2,
          activation = !!fn)  %>%
        set_engine("keras", verbose = 0) %>%
        parsnip::fit(Class ~ A + B, data = modeldata::two_class_dat),
      silent = TRUE)

  }
  test_act_sshhh <- purrr::quietly(test_act)

  for (i in act) {
    keras::backend()$clear_session()
    act_res <- test_act_sshhh(i)
    expect_s3_class(act_res$result, "model_fit")
    keras::backend()$clear_session()
  }

  expect_snapshot(
    error = TRUE,
    mlp(mode = "classification", hidden_units = 2, penalty = 0.01, epochs = 2,
          activation = "invalid")  %>%
      set_engine("keras", verbose = 0) %>%
      parsnip::fit(Class ~ A + B, data = modeldata::two_class_dat)
  )
})