test_that("enet regression works", {
  library(dplyr)
  df_reg <- MASS::Boston
  df_cls3 <- iris
  df_cls2 <- iris %>% filter(Species != "setosa")

  m_reg <- m("enet", medv ~ ., df_reg, lambda = c(0.1), alpha = 0.1)
  expect_equal(nrow(coef(m_reg)), 12)
  expect_equal(nrow(predict(m_reg, df_reg)), 506)
  expect_equal(nrow(fitted(m_reg)), 506)
  expect_equal(nrow(resid(m_reg)), 506)
  expect_equal(nrow(predict(m_reg, df_reg %>% select(-medv))), 506)

})

test_that("enet classification works", {
  library(dplyr)
  df_cls3 <- iris
  df_cls2 <- iris %>% filter(Species != "setosa")

  m_reg <- classify(df_cls3, Species ~ ., m("enet", lambda = c(0.1), alpha = 0.1))
  expect_equal(nrow(coef(m_reg)), 14)
  expect_equal(nrow(predict(m_reg, df_cls3)), 450)
  expect_equal(nrow(fitted(m_reg)), 450)
  expect_equal(nrow(predict(m_reg, df_cls3 %>% select(-Species))), 450)

})

test_that("enet classification (2class) works", {
  library(dplyr)
  df_cls3 <- iris
  df_cls2 <- iris %>% filter(Species != "virginica")

  m_reg <- classify(df_cls2, Species ~ ., m("enet", lambda = c(0.01), alpha = 0.1))
  expect_equal(nrow(coef(m_reg)), 5)
  expect_equal(nrow(predict(m_reg, df_cls2)), 100)
  expect_equal(nrow(fitted(m_reg)), 100)
  expect_equal(nrow(predict(m_reg, df_cls2 %>% select(-Species))), 100)

})