tests/testthat/test-swissmetro-b09.R

In rbiogeme: Interface to 'Biogeme'

native_swissmetro_b09 <- function(data) {
  expressions <- reticulate::import("biogeme.expressions", convert = FALSE)
  database_module <- reticulate::import("biogeme.database", convert = FALSE)
  biogeme_module <- reticulate::import("biogeme.biogeme", convert = FALSE)
  models <- reticulate::import("biogeme.models", convert = FALSE)
  nests_module <- reticulate::import("biogeme.nests", convert = FALSE)
  bridge <- rbiogeme:::biogeme_bridge()

  database <- database_module$Database(
    "swissmetro_native_b09",
    reticulate::r_to_py(data)
  )
  variable <- expressions$Variable
  purpose <- variable("PURPOSE")
  choice <- variable("CHOICE")
  database$remove(((purpose != 1) * (purpose != 3) + (choice == 0)) > 0)

  ga <- variable("GA")
  sp <- variable("SP")
  sm_cost <- database$define_variable("SM_COST", variable("SM_CO") * (ga == 0))
  train_cost <- database$define_variable("TRAIN_COST", variable("TRAIN_CO") * (ga == 0))
  car_av_sp <- database$define_variable("CAR_AV_SP", variable("CAR_AV") * (sp != 0))
  train_av_sp <- database$define_variable("TRAIN_AV_SP", variable("TRAIN_AV") * (sp != 0))
  train_tt_scaled <- database$define_variable("TRAIN_TT_SCALED", variable("TRAIN_TT") / 100)
  train_cost_scaled <- database$define_variable("TRAIN_COST_SCALED", train_cost / 100)
  sm_tt_scaled <- database$define_variable("SM_TT_SCALED", variable("SM_TT") / 100)
  sm_cost_scaled <- database$define_variable("SM_COST_SCALED", sm_cost / 100)
  car_tt_scaled <- database$define_variable("CAR_TT_SCALED", variable("CAR_TT") / 100)
  car_co_scaled <- database$define_variable("CAR_CO_SCALED", variable("CAR_CO") / 100)

  beta <- expressions$Beta
  asc_car <- beta("asc_car", 0, NULL, NULL, 0)
  asc_train <- beta("asc_train", 0, NULL, NULL, 0)
  asc_sm <- beta("asc_sm", 0, NULL, NULL, 1)
  b_time <- beta("b_time", 0, NULL, NULL, 0)
  b_cost <- beta("b_cost", 0, NULL, NULL, 0)
  nest_parameter <- beta("nest_parameter", 1, 1, 3, 0)
  utilities <- reticulate::dict(
    `1` = asc_train + b_time * train_tt_scaled + b_cost * train_cost_scaled,
    `2` = asc_sm + b_time * sm_tt_scaled + b_cost * sm_cost_scaled,
    `3` = asc_car + b_time * car_tt_scaled + b_cost * car_co_scaled
  )
  availability <- reticulate::dict(
    `1` = train_av_sp,
    `2` = variable("SM_AV"),
    `3` = car_av_sp
  )
  existing <- nests_module$OneNestForNestedLogit(
    nest_param = nest_parameter,
    list_of_alternatives = reticulate::r_to_py(as.integer(c(1L, 3L))),
    name = "existing"
  )
  nests <- nests_module$NestsForNestedLogit(
    choice_set = reticulate::r_to_py(as.integer(c(1L, 2L, 3L))),
    tuple_of_nests = reticulate::tuple(existing)
  )
  log_probability <- models$lognested(utilities, availability, nests, choice)
  biogeme <- biogeme_module$BIOGEME(
    database,
    log_probability,
    optimization_algorithm = "simple_bounds_BFGS",
    generate_html = FALSE,
    generate_yaml = FALSE,
    save_iterations = FALSE
  )
  biogeme$model_name <- "b09_nested"
  biogeme$calculate_null_loglikelihood(availability)
  results <- biogeme$estimate()
  extracted <- reticulate::py_to_r(bridge$extract_estimation_results(results))
  beta_values <- extracted$beta_values
  names(beta_values) <- extracted$beta_names
  alternatives_names <- c(`1` = "Train", `2` = "Swissmetro", `3` = "Car")
  correlation <- reticulate::py_to_r(nests$correlation(
    parameters = reticulate::r_to_py(as.list(beta_values)),
    alternatives_names = reticulate::r_to_py(as.list(alternatives_names))
  ))
  list(
    results = extracted,
    correlation = as.matrix(correlation),
    number_of_rows = nrow(reticulate::py_to_r(database$dataframe))
  )
}

test_that("b09 Swissmetro nested logit matches native Biogeme", {
  skip_if_not(
    identical(Sys.getenv("RBIOGEME_RUN_INTEGRATION"), "1"),
    "Set RBIOGEME_RUN_INTEGRATION=1 to run full Swissmetro equivalence tests"
  )
  skip_if_not(
    rbiogeme_test_configure_python(),
    "Set RBIOGEME_PYTHON to a compatible native Biogeme interpreter"
  )
  data_path <- rbiogeme_test_swissmetro_path()
  skip_if(
    !nzchar(data_path),
    "Set RBIOGEME_SWISSMETRO_DATA to the Swissmetro .dat file"
  )

  data <- read.delim(data_path, check.names = FALSE, stringsAsFactors = FALSE)
  database <- swissmetro_data(data)
  nest_parameter <- biogeme_beta(
    "nest_parameter",
    start = 1,
    lower = 1,
    upper = 3
  )
  model <- nested_logit_model(
    database = database,
    choice = "CHOICE",
    utilities = list(
      `1` = biogeme_beta("asc_train", start = 0) +
        biogeme_beta("b_time", start = 0) * variable("TRAIN_TT_SCALED") +
        biogeme_beta("b_cost", start = 0) * variable("TRAIN_COST_SCALED"),
      `2` = biogeme_beta("asc_sm", start = 0, fixed = TRUE) +
        biogeme_beta("b_time", start = 0) * variable("SM_TT_SCALED") +
        biogeme_beta("b_cost", start = 0) * variable("SM_COST_SCALED"),
      `3` = biogeme_beta("asc_car", start = 0) +
        biogeme_beta("b_time", start = 0) * variable("CAR_TT_SCALED") +
        biogeme_beta("b_cost", start = 0) * variable("CAR_CO_SCALED")
    ),
    availability = list(
      `1` = variable("TRAIN_AV_SP"),
      `2` = variable("SM_AV"),
      `3` = variable("CAR_AV_SP")
    ),
    nests = nested_nests(
      choice_set = c(1L, 2L, 3L),
      nests = list(nested_nest(nest_parameter, c(1L, 3L), name = "existing"))
    ),
    control = biogeme_control(
      model_name = "b09_nested",
      optimization_algorithm = "simple_bounds_BFGS",
      generate_html = FALSE,
      generate_yaml = FALSE,
      save_iterations = FALSE
    )
  )

  temporary_directory <- tempfile("rbiogeme-b09-")
  dir.create(temporary_directory, recursive = TRUE)
  original_directory <- getwd()
  setwd(temporary_directory)
  on.exit(setwd(original_directory), add = TRUE)

  r_fit <- estimate(
    model,
    model_name = "b09_nested",
    control = model$control
  )
  r_correlation <- nested_logit_correlation(
    model,
    beta_values = coef(r_fit),
    alternatives_names = c(`1` = "Train", `2` = "Swissmetro", `3` = "Car")
  )
  native <- native_swissmetro_b09(data)
  native_results <- native$results

  expect_equal(nobs(r_fit), native$number_of_rows)
  expect_identical(r_fit$beta_names, native_results$beta_names)
  expect_equal(unname(coef(r_fit)), native_results$beta_values, tolerance = 1e-8)
  expect_equal(
    as.numeric(logLik(r_fit)),
    native_results$final_log_likelihood,
    tolerance = 1e-8
  )
  expect_equal(r_fit$number_of_excluded_data, native_results$number_of_excluded_data)
  expect_identical(isTRUE(r_fit$convergence), isTRUE(native_results$convergence))
  expect_equal(unname(r_correlation), unname(native$correlation), tolerance = 1e-8)
})