context("Test deeptrafo residuals")

if (FALSE) {

  test_that("residuals coincide with tram (ordinal)", {
    library("tram")

    data("wine", package = "ordinal")
    tm <- Polr(rating ~ temp + contact, data = wine)

    m <- deeptrafo(rating ~ 0 + temp + contact, data = wine,
                   optimizer = optimizer_adam(learning_rate = 0.1, decay = 1e-4))
    fit(m, epochs = 3e2, validation_split = NULL, batch_size = nrow(wine),
        verbose = FALSE)

    expect_equal(residuals(m), residuals(tm), tolerance = 1e-4)
  })

  # test_that("residuals coincide with tram (ordinal, rv)", {
  #   library(tram)
  #   tn <- 1e2
  #   df <- data.frame(y = ordered(sample.int(5, tn, TRUE)),
  #                    x = factor(sample.int(2, tn, TRUE)),
  #                    z = factor(sample.int(2, tn, TRUE)))
  #
  #   tm <- Polr(y | 0 + x ~ z, data = df)
  #   m <- deeptrafo(y | x ~ 0 + z, data = df, optimizer = optimizer_adam(
  #     learning_rate = 0.1, decay = 1e-3))
  #   fit(m, epochs = 3e3, validation_split = NULL, batch_size = tn)
  #
  #   nd <- expand.grid(y = sort(unique(df$y)),
  #                     x = sort(unique(df$x)),
  #                     z = sort(unique(df$z)))
  #   nd$deeptrafo <- residuals(m, newdata = nd)
  #   nd$tram <- residuals(tm, newdata = nd)
  #   nd
  #
  #   expect_equal(residuals(m), residuals(tm), tolerance = 1e-1)
  # })

  test_that("residuals coincide with tram (continuous)", {
    library(tram)

    tm <- Lm(dist ~ speed, data = cars, support = range(cars$dist))
    m <- LmNN(dist ~ 0 + speed, data = cars)

    cfx <- coef(tm, with_baseline = TRUE)
    tmp <- get_weights(m$model)
    tmp[[1]][] <- c(cfx[1], log(exp(cfx[2]) - 1))
    tmp[[2]][] <- -cfx[length(cfx)]
    set_weights(m$model, tmp)

    expect_equal(residuals(m), residuals(tm), tolerance = 1e-4)
  })

  test_that("residuals coincide with tram (continuous, Bernstein)", {
    library(tram)

    tm <- Colr(dist ~ speed, data = cars, support = range(cars$dist))
    m <- ColrNN(dist ~ 0 + speed, data = cars, order = 6)

    cfx <- coef(tm, with_baseline = TRUE)
    tmp <- get_weights(m$model)
    tmp[[1]][] <- c(cfx[1], log(exp(diff(cfx[-length(cfx)])) - 1))
    tmp[[2]][] <- cfx[length(cfx)]
    set_weights(m$model, tmp)

    expect_equal(
      unname(c(unlist(coef(m, which = "interacting")), unlist(coef(m)))),
      unname(cfx), tol = 1e-5
    )

    expect_equal(residuals(m), residuals(tm), tolerance = 1e-4)
  })

}