tests/testthat/test-estimate_loss.R

In rangi513/grizbayr: Bayesian Inference for A|B and Bandit Marketing Tests

context("Estimate Loss Distribution")

test_that("estimate_loss returns double vector the same length as the rows in posterior_samples input", {
  distribution_type <- "conversion_rate"
  input_df <- tibble::tibble(
    option_name = c("A", "B", "C"),
    sum_clicks = c(1000, 1000, 1000),
    sum_conversions = c(100, 120, 110)
  )
  posterior_samples <- sample_from_posterior(input_df, distribution_type, priors = list())
  count_unique_options <- length(unique(posterior_samples$option_name))
  posterior_samples_rows <- nrow(posterior_samples)/count_unique_options

  output <- estimate_loss(posterior_samples = posterior_samples,
                          distribution = distribution_type)
  expect_length(output, posterior_samples_rows)
  expect_true(is.double(output))
})


test_that("estimate_loss returns double vector when wrt_option is set", {
  distribution_type <- "conversion_rate"
  input_df <- tibble::tibble(
    option_name = c("A", "B", "C"),
    sum_clicks = c(1000, 1000, 1000),
    sum_conversions = c(100, 120, 110)
  )
  posterior_samples <- sample_from_posterior(input_df, distribution_type, priors = list())
  count_unique_options <- length(unique(posterior_samples$option_name))
  posterior_samples_rows <- nrow(posterior_samples)/count_unique_options

  output <- estimate_loss(posterior_samples = posterior_samples,
                          distribution = distribution_type,
                          wrt_option = "C")
  expect_length(output, posterior_samples_rows)
  expect_true(is.double(output))
})

test_that("estimate_loss returns double vector the same length
          as the rows in posterior_samples input
          when winner_is_min", {
  distribution_type <- "cpa"
  input_df <- tibble::tibble(
    option_name = c("A", "B", "C"),
    sum_clicks = c(1000, 1000, 1000),
    sum_conversions = c(100, 120, 110),
    sum_cost = c(50, 100, 150),
  )
  posterior_samples <- sample_from_posterior(input_df, distribution_type, priors = list())
  count_unique_options <- length(unique(posterior_samples$option_name))
  posterior_samples_rows <- nrow(posterior_samples)/count_unique_options

  output <- estimate_loss(posterior_samples = posterior_samples,
                          distribution = distribution_type)
  expect_length(output, posterior_samples_rows)
  expect_true(is.double(output))
})