test-swissmetro-b19.R
In rbiogeme: Interface to 'Biogeme'

native_swissmetro_b19 <- function(data, number_of_draws = 128L, seed = 1223L) {
  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)
  bridge <- rbiogeme:::biogeme_bridge()

  database <- database_module$Database(
    "swissmetro_native_b19",
    reticulate::r_to_py(data)
  )
  variable <- expressions$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$Beta
  asc_car <- beta("asc_car", 0, NULL, NULL, 0)
  asc_train <- beta("asc_train", 0, NULL, NULL, 0)
  asc_sm <- beta("asc_sm", 0, NULL, NULL, 1)
  b_cost <- beta("b_cost", 0, NULL, NULL, 0)
  b_time <- beta("b_time", 0, NULL, NULL, 0)
  b_time_s <- beta("b_time_s", 1, NULL, NULL, 0)
  b_time_rnd <- b_time + b_time_s * expressions$Draws("b_time_rnd", "NORMAL")
  utilities <- reticulate::dict(
    `1` = asc_train + b_time_rnd * train_tt_scaled + b_cost * train_cost_scaled,
    `2` = asc_sm + b_time_rnd * sm_tt_scaled + b_cost * sm_cost_scaled,
    `3` = asc_car + b_time_rnd * car_tt_scaled + b_cost * car_co_scaled
  )
  availability <- reticulate::dict(
    `1` = train_av_sp,
    `2` = variable("SM_AV"),
    `3` = car_av_sp
  )
  prob_chosen <- models$logit(utilities, availability, choice)
  estimator <- biogeme_module$BIOGEME(
    database,
    expressions$log(expressions$MonteCarlo(prob_chosen)),
    number_of_draws = as.integer(number_of_draws),
    seed = as.integer(seed),
    analytical_hessian_mode = "automatic",
    generate_html = FALSE,
    generate_yaml = FALSE,
    save_iterations = FALSE
  )
  estimator$model_name <- "b05a_normal_mixture"
  estimation_results <- estimator$estimate()
  betas <- estimation_results$get_beta_values()
  simulations <- reticulate::dict(
    Numerator = expressions$MonteCarlo(b_time_rnd * prob_chosen),
    Denominator = expressions$MonteCarlo(prob_chosen),
    Choice = choice
  )
  simulator <- biogeme_module$BIOGEME(
    database,
    simulations,
    number_of_draws = as.integer(number_of_draws),
    seed = as.integer(seed),
    generate_html = FALSE,
    generate_yaml = FALSE,
    save_iterations = FALSE
  )
  simulator$model_name <- "b19_individual_level_parameters"
  simulated <- reticulate::py_to_r(simulator$simulate(the_beta_values = betas))
  simulated[["Individual-level parameters"]] <-
    simulated[["Numerator"]] / simulated[["Denominator"]]
  list(
    results = reticulate::py_to_r(bridge$extract_estimation_results(estimation_results)),
    values = simulated,
    number_of_rows = nrow(reticulate::py_to_r(database$dataframe)),
    number_of_draws = as.integer(number_of_draws)
  )
}

test_that("b19 Swissmetro individual-level parameters match 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)
  asc_car <- biogeme_beta("asc_car", start = 0)
  asc_train <- biogeme_beta("asc_train", start = 0)
  asc_sm <- biogeme_beta("asc_sm", start = 0, fixed = TRUE)
  b_cost <- biogeme_beta("b_cost", start = 0)
  b_time <- biogeme_beta("b_time", start = 0)
  b_time_s <- biogeme_beta("b_time_s", start = 1)
  b_time_rnd <- b_time + b_time_s * draw("b_time_rnd", "NORMAL")
  utilities <- list(
    `1` = asc_train + b_time_rnd * variable("TRAIN_TT_SCALED") +
      b_cost * variable("TRAIN_COST_SCALED"),
    `2` = asc_sm + b_time_rnd * variable("SM_TT_SCALED") +
      b_cost * variable("SM_COST_SCALED"),
    `3` = asc_car + b_time_rnd * variable("CAR_TT_SCALED") +
      b_cost * variable("CAR_CO_SCALED")
  )
  availability <- list(
    `1` = variable("TRAIN_AV_SP"),
    `2` = variable("SM_AV"),
    `3` = variable("CAR_AV_SP")
  )
  probability <- logit_probability(utilities, availability, variable("CHOICE"))
  simulations <- list(
    Numerator = monte_carlo(b_time_rnd * probability),
    Denominator = monte_carlo(probability),
    Choice = variable("CHOICE")
  )
  draws <- biogeme_draws("b_time_rnd", "NORMAL", 128L, 1223L)
  model <- biogeme_model(
    database = database,
    formula = log(monte_carlo(probability)),
    draws = draws
  )

  temporary_directory <- tempfile("rbiogeme-b19-")
  dir.create(temporary_directory, recursive = TRUE)
  original_directory <- getwd()
  setwd(temporary_directory)
  on.exit(setwd(original_directory), add = TRUE)
  controls <- biogeme_control(
    model_name = "b05a_normal_mixture",
    number_of_draws = 128L,
    seed = 1223L,
    analytical_hessian_mode = "automatic",
    generate_html = FALSE,
    generate_yaml = FALSE,
    save_iterations = FALSE
  )
  r_fit <- estimate(model, model_name = "b05a_normal_mixture", control = controls)
  model$simulations <- simulations
  r_values <- as.data.frame(simulate(
    model,
    beta = r_fit,
    control = biogeme_control(
      model_name = "b19_individual_level_parameters",
      number_of_draws = 128L,
      seed = 1223L,
      generate_html = FALSE,
      generate_yaml = FALSE,
      save_iterations = FALSE
    )
  ), check.names = FALSE)
  r_values[["Individual-level parameters"]] <-
    r_values[["Numerator"]] / r_values[["Denominator"]]

  native <- native_swissmetro_b19(data, 128L, 1223L)
  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$beta_values, tolerance = 1e-7)
  expect_equal(as.numeric(logLik(r_fit)), native_results$final_log_likelihood, tolerance = 1e-7)
  expect_identical(names(r_values), names(native$values))
  expect_equal(
    unname(as.matrix(r_values)),
    unname(as.matrix(native$values)),
    tolerance = 1e-7,
    ignore_attr = TRUE
  )
  expect_equal(r_fit$number_of_draws, native_results$number_of_draws)
})