tests/testthat/test-glm_fast.R

In tlverse/sl3: Pipelines for Machine Learning and Super Learning

context("test-glm_fast.R -- Lrnr_glm_fast")

if (FALSE) {
  setwd("..")
  setwd("..")
  getwd()
  library("devtools")
  document()
  load_all("./") # load all R files in /R and datasets in /data. Ignores NAMESPACE:
  devtools::check() # runs full check
  setwd("..")
  install("sl3", build_vignettes = FALSE, dependencies = FALSE) # INSTALL W/ devtools:
}

# library(data.table) library(origami)
library(SuperLearner)
set.seed(1)

data(cpp_imputed)
covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
outcome <- "haz"

task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)

interactions <- list(c("apgar1", "apgar5"))
task_with_interactions <- task$add_interactions(interactions)

test_that("Lrnr_glm_fast works with empty X (intercept-only)", {
  fglm_learner <- Lrnr_glm_fast$new()
  empty_task <- sl3_Task$new(cpp_imputed, covariates = NULL, outcome = outcome)
  fGLM_fit <- fglm_learner$base_train(empty_task)
  fglm_preds <- fGLM_fit$predict()
  expect_equal(length(unique(fglm_preds)), 1)
})

test_that("Lrnr_glm and Lrnr_glm_fast works with empty X (intercept-only)", {
  glm_learner <- Lrnr_glm$new()
  fglm_learner <- Lrnr_glm_fast$new()
  GLM_fit <- glm_learner$train(task)
  glm_preds <- GLM_fit$predict()
  fGLM_fit <- fglm_learner$train(task)
  fglm_preds <- fGLM_fit$predict()
  expect_true(all.equal(as.vector(glm_preds), as.vector(fglm_preds)))
})

test_that("Lrnr_glm_fast trains on a subset of covariates (predictors)", {
  fglm_learner <- Lrnr_glm_fast$new(covariates = c("apgar1", "apgar5", "apgar1_apgar5"))
  fGLM_fit <- fglm_learner$train(task_with_interactions)

  # print(fGLM_fit) str(fGLM_fit$params)
  fglm_preds_3 <- fGLM_fit$predict()

  glm.fit <- glm(haz ~ apgar1 + apgar5 + apgar1:apgar5, data = cpp_imputed, family = stats::gaussian())
  # print(glm.fit)
  glm_preds_3 <- as.vector(predict(glm.fit))

  expect_true(sum(fglm_preds_3 - glm_preds_3) < 10^(-10))
  expect_true(all.equal(as.vector(glm_preds_3), as.vector(fglm_preds_3)))
})

test_that("Lrnr_glm_fast works with screener", {
  # example of learner chaining
  slscreener <- Lrnr_pkg_SuperLearner_screener$new("screen.glmnet")

  ## FAILS, because screener currently renames the covariates fglm_learner <-
  ## Lrnr_glm_fast$new(covariates = c('apgar1', 'meducyrs'), interactions =
  ## list(c('apgar1', 'meducyrs')))
  fglm_learner <- Lrnr_glm_fast$new()
  screen_and_glm <- Pipeline$new(slscreener, fglm_learner)
  sg_fit <- screen_and_glm$train(task)
  preds <- sg_fit$predict()
  expect_equal(length(preds), nrow(task$data))
})

test_that("Lrnr_glm_fast works with stacking", {
  glm_learner <- Lrnr_glm$new()
  fglm_learner <- Lrnr_glm_fast$new()
  screen_and_glm <- Pipeline$new(
    Lrnr_pkg_SuperLearner_screener$new("screen.glmnet"),
    fglm_learner
  )
  SL.glmnet_learner <- Lrnr_pkg_SuperLearner$new(SL_wrapper = "SL.glmnet")

  # now lets stack some learners
  learner_stack <- Stack$new(glm_learner, fglm_learner, screen_and_glm, SL.glmnet_learner)
  stack_fit <- learner_stack$train(task)
  # print(stack_fit)
  preds <- stack_fit$predict()
  # print(head(preds))
  expect_equal(nrow(preds), nrow(task$data))
  expect_equal(ncol(preds), 4)
})

test_that("Lrnr_glm_fast works with quasibinomial and continuous outcomes in (0,1)", {
  cpp_haz_01range <- cpp_imputed
  cpp_haz_01range[["haz_01range"]] <- rep_len(c(0.1, 0.9), nrow(cpp_imputed))
  task_01range <- sl3_Task$new(cpp_haz_01range, covariates = covars, outcome = "haz_01range")

  fglm_learner <- Lrnr_glm_fast$new(family = quasibinomial())
  fGLM_fit <- fglm_learner$train(task_01range)
  # print(fGLM_fit)
  fGLM_pred <- fGLM_fit$predict()
  expect_equal(length(fGLM_pred), nrow(task_01range$data))
  expect_true(all(min(fGLM_pred) >= 0.1, max(fGLM_pred) <= 0.9))

  fglm_learner <- Lrnr_glm_fast$new(family = binomial())
  fGLM_fit <- fglm_learner$train(task_01range)
  # print(fGLM_fit)
  fGLM_pred <- fGLM_fit$predict()
  expect_equal(length(fGLM_pred), nrow(task_01range$data))
  expect_true(all(min(fGLM_pred) >= 0.1, max(fGLM_pred) <= 0.9))
})

test_that("Lrnr_glm_fast works with different families ('family = ...') and solvers ('method = ...')", {
  cpp_hazbin <- cpp_imputed
  cpp_hazbin[["haz_bin"]] <- rep_len(c(0L, 1L), nrow(cpp_imputed))
  task_bin <- sl3_Task$new(cpp_hazbin, covariates = covars, outcome = "haz_bin")

  fglm_learner <- Lrnr_glm_fast$new(family = quasibinomial())
  fGLM_fit <- fglm_learner$train(task_bin)
  # print(fGLM_fit)
  fGLM_pred <- fGLM_fit$predict()
  expect_equal(length(fGLM_pred), nrow(task_bin$data))

  fglm_learner <- Lrnr_glm_fast$new(family = binomial())
  fGLM_fit <- fglm_learner$train(task_bin)
  # print(fGLM_fit)
  fGLM_pred <- fGLM_fit$predict()
  expect_equal(length(fGLM_pred), nrow(task_bin$data))

  fglm_learner <- Lrnr_glm_fast$new(family = binomial(), method = "eigen")
  fGLM_fit <- fglm_learner$train(task_bin)
  # print(fGLM_fit)
  fGLM_pred <- fGLM_fit$predict()
  expect_equal(length(fGLM_pred), nrow(task_bin$data))

  fglm_learner <- Lrnr_glm_fast$new(family = binomial(), method = "Cholesky")
  fGLM_fit <- fglm_learner$train(task_bin)
  # print(fGLM_fit)
  fGLM_pred <- fGLM_fit$predict()
  expect_equal(length(fGLM_pred), nrow(task_bin$data))

  fglm_learner <- Lrnr_glm_fast$new(family = binomial(), method = "qr")
  fGLM_fit <- fglm_learner$train(task_bin)
  # print(fGLM_fit)
  fGLM_pred <- fGLM_fit$predict()
  expect_equal(length(fGLM_pred), nrow(task_bin$data))
})

test_that("When speedglm fails (singlular X) the fallback glm works", {
  ## make a singular X for testing:
  set.seed(123456)
  dat_test <- data.frame(Y = rep(0L, 100), X1 = rnorm(100), X2 = rnorm(100))
  dat_test <- cbind(dat_test, X3 = dat_test[["X1"]] + dat_test[["X2"]])
  task_all <- sl3_Task$new(dat_test, covariates = c("X1", "X2", "X3"), outcome = "Y")
  glm_lrnr <- Lrnr_glm$new()$train(task_all)
  fglm_lrnr <- Lrnr_glm_fast$new(method = "Cholesky")$train(task_all)
  glm_preds <- glm_lrnr$predict()
  fglm_preds <- fglm_lrnr$predict()
  expect_true(all.equal(as.vector(glm_preds), as.vector(fglm_preds)))
})