| context("Task Parameter Estimation") |
|
|
| loadExamples(4) |
| setParameterEstimationSettings(update_model = FALSE, method = "LevenbergMarquardt") |
|
|
| test_that("runParameterEstimation()", { |
| PE <- runParameterEstimation() |
| expect_length(PE, 13) |
| |
| expect_is(PE$settings, "list") |
| |
| expect_is(PE$main, "list") |
| expect_gt(PE$main$objective_value, 0) |
| expect_gt(PE$main$root_mean_square, 0) |
| expect_gt(PE$main$standard_deviation, 0) |
| expect_gte(PE$main$validation_objective_value, 0) |
| expect_identical(PE$main$validation_root_mean_square, NaN) |
| expect_identical(PE$main$validation_standard_deviation, NaN) |
| expect_gt(PE$main$function_evaluations, 0) |
| expect_gt(PE$main$cpu_time_s, 0) |
| expect_gt(PE$main$evaluations_second_1_s, 0) |
| |
| param_count <- 3L |
| |
| expect_length(PE$parameters, 8) |
| expect_equal(nrow(PE$parameters), param_count) |
| |
| expect_length(PE$experiments, 5) |
| expect_equal(nrow(PE$experiments), 1L) |
| |
| expect_length(PE$fitted_values, 5) |
| expect_equal(nrow(PE$fitted_values), 2L) |
| |
| expect_equal(ncol(PE$correlation), param_count) |
| expect_equal(nrow(PE$correlation), param_count) |
| |
| expect_equal(ncol(PE$fim), param_count) |
| expect_equal(nrow(PE$fim), param_count) |
| |
| expect_equal(ncol(PE$fim_eigenvalues), 1L) |
| expect_equal(nrow(PE$fim_eigenvalues), param_count) |
| |
| expect_equal(ncol(PE$fim_eigenvectors), param_count) |
| expect_equal(nrow(PE$fim_eigenvectors), param_count) |
| |
| expect_equal(ncol(PE$fim_scaled), param_count) |
| expect_equal(nrow(PE$fim_scaled), param_count) |
| |
| expect_equal(ncol(PE$fim_scaled_eigenvalues), 1L) |
| expect_equal(nrow(PE$fim_scaled_eigenvalues), param_count) |
| |
| expect_equal(ncol(PE$fim_scaled_eigenvectors), param_count) |
| expect_equal(nrow(PE$fim_scaled_eigenvectors), param_count) |
| |
| expect_is(PE$protocol, "character") |
| }) |
|
|
| test_that("ParameterEstimation parameter start value is valid", { |
| PE <- runParameterEstimation(update_model = FALSE) |
| |
| expect_equal(PE$parameters$start_value, c(1.00, 0.25, 0.10)) |
| |
| PE2 <- runParameterEstimation(update_model = TRUE) |
| |
| expect_identical(PE$parameters, PE2$parameters) |
| expect_identical(PE$experiments, PE2$experiments) |
| }) |
|
|
| unloadAllModels() |
| loadExamples(4) |
| setParameterEstimationSettings(update_model = FALSE, method = "LevenbergMarquardt") |
|
|
| test_that("ParameterEstimation Experimental Data", { |
| |
| ts <- list( |
| tibble::tibble( |
| time = c(0L, 1L, 2L, 3L), |
| a = c(0, 9, 15, 18), |
| b = c(0, 1, NA, 12), |
| c = NA, |
| d = c(23456, 1235321456.2165, 131854, NA), |
| e = NA_real_, |
| f = 1 |
| ), |
| tibble::tibble( |
| time = c(0L, 1L, 2L, 3L), |
| a = c(0, NaN, 15, 18), |
| b = c(0, 1, 5, 12), |
| f = 2 |
| ) |
| ) |
| |
| exp_path <- tempfile(fileext = ".txt") |
| exp <- defineExperiments( |
| experiment_type = "time_course", |
| data = ts, |
| types = c("time", "dependent", "dependent", "dependent", "ignore", "ignore", "independent"), |
| mappings = c(NA, "{[A]}", "{[B]}", compartment_strict("compartment", reference = "Volume"), NA, NA, compartment_strict("compartment", reference = "InitialVolume")), |
| filename = exp_path |
| ) |
| |
| clearExperiments() |
| addExperiments(exp) |
| |
| |
| ts_merged <- vctrs::vec_rbind(!!!ts) |
| ts_readin <- tibble::as_tibble(utils::read.table(exp_path, header = TRUE, sep = "\t", colClasses = purrr::map_chr(ts_merged, typeof))) |
| |
| expect_identical(ts_merged, ts_readin) |
| |
| PE <- runParameterEstimation() |
| |
| expect_equal(nrow(PE$parameters), 3L) |
| expect_equal(nrow(PE$experiments), 2L) |
| expect_equal(nrow(PE$fitted_values), 3L) |
| |
| clearExperiments() |
| file.remove(exp_path) |
| }) |
|
|
| test_that("setOptimizationSettings() method", { |
| expect_error(setParameterEstimationSettings(method = "...")) |
| setParameterEstimationSettings(method = list()) |
| expect_error(setParameterEstimationSettings(method = list("."))) |
| }) |
|
|
| unloadAllModels() |
| clearCustomKineticFunctions() |
|
|
