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Merge pull request #29 from MatsuuraKentaro/test-generate-setup-code
add tests for generate_setup_code
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| # Base -------------------------------------------------------------------- | ||
| doses <- c(0, 2, 4, 6, 8) | ||
| models <- DoseFinding::Mods( | ||
| doses = doses, maxEff = 1.65, linear = NULL, emax = 0.79, sigEmax = c(4, 5) | ||
| ) | ||
| Delta <- 1.3 | ||
|
|
||
| obj <- new.env() | ||
| code <- generate_setup_code( | ||
| doses, models, Delta, outcome_type = "continuous", | ||
| optimization_metric = "MAE", rl_models = models,seed = 123, alpha = 0.025, | ||
| selModel = "AIC", Delta_range = c(0.9, 1.1) * Delta) | ||
| eval(code, obj) | ||
|
|
||
| test_that("compute_reward_TD estimated_target_dose = NA", { | ||
| act <- obj$compute_reward_TD(NA, "linear") | ||
| expect_equal(act, expected = 0) | ||
| }) | ||
|
|
||
| test_that("compute_reward_TD", { | ||
| act <- obj$compute_reward_TD(5, "linear") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(6, "linear") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(7, "linear") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(1, "emax") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(2, "emax") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(3, "emax") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(4, "sigEmax") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(5, "sigEmax") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(6, "sigEmax") | ||
| expect_equal(act, expected = 0) | ||
| }) | ||
|
|
||
| test_that("compute_MAE", { | ||
| set.seed(123) | ||
| x <- rnorm(10) | ||
| y <- rnorm(10) | ||
| act <- obj$compute_MAE(x, y) | ||
| expect_equal(act, expected = 1.8339568) | ||
| }) | ||
|
|
||
| test_that("compute_reward_MAE", { | ||
| act <- obj$compute_reward_MAE(0.1) | ||
| expect_equal(act, expected = 0.87878788) | ||
| }) | ||
|
|
||
| test_that("compute_reward fit well", { | ||
| model_names <- c("linear", "emax", "sigEmax") | ||
| optimization_metrics <- c("MAE", "power", "TD", "power and MAE") | ||
|
|
||
| df_expected <- data.frame( | ||
| linear = c(0.98473088, 1, 1, 0.98473088), | ||
| emax = c(0.97718847, 1, 1, 0.97718847), | ||
| sigEmax = c(0.9839088, 1, 1, 0.9839088), # MacOS: 0.983908858 | ||
| row.names = optimization_metrics | ||
| ) | ||
|
|
||
| for (model_name in model_names) { | ||
| set.seed(123) | ||
| sim_doses <- sample(doses, 10, replace = TRUE) | ||
| actions <- seq_along(obj$doses) | ||
| names(actions) <- obj$doses | ||
| sim_actions <- actions[as.character(sim_doses)] | ||
| true_resps <- obj$true_response_list[[model_name]][sim_actions] | ||
| sim_resps <- rnorm(10, mean = true_resps, sd = 0.01) | ||
|
|
||
| for (optimization_metric in optimization_metrics) { | ||
| obj$optimization_metric <- optimization_metric | ||
| act <- obj$compute_reward(model_name, sim_doses, sim_resps) | ||
| expected <- df_expected[optimization_metric, model_name] | ||
| expect_equal(act, expected, tolerance = 1e-7) | ||
| } | ||
| } | ||
|
|
||
| obj$optimization_metric <- "MAE" | ||
| }) | ||
|
|
||
| test_that("compute_reward don't fit well", { | ||
| model_names <- c("linear", "emax", "sigEmax") | ||
| optimization_metrics <- c("MAE", "power", "TD", "power and MAE") | ||
|
|
||
| df_expected <- data.frame( | ||
| linear = c(0.16667721, 0, 0, 0), | ||
| emax = c(-0.64942815, 0, 0, 0), | ||
| sigEmax = c(0.36050607, 0, 0, 0), | ||
| row.names = optimization_metrics | ||
| ) | ||
|
|
||
| for (model_name in model_names) { | ||
| set.seed(123) | ||
| sim_doses <- sample(doses, 10, replace = TRUE) | ||
| actions <- seq_along(obj$doses) | ||
| names(actions) <- obj$doses | ||
| sim_actions <- actions[as.character(sim_doses)] | ||
| true_resps <- obj$true_response_list[[model_name]][sim_actions] | ||
| sim_resps <- rnorm(10, mean = true_resps, sd = 0.5) | ||
|
|
||
| for (optimization_metric in optimization_metrics) { | ||
| obj$optimization_metric <- optimization_metric | ||
| act <- obj$compute_reward(model_name, sim_doses, sim_resps) | ||
| expected <- df_expected[optimization_metric, model_name] | ||
| expect_equal(act, expected) | ||
| } | ||
| } | ||
|
|
||
| obj$optimization_metric <- "MAE" | ||
| }) | ||
|
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||
|
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||
| # Large Delta ------------------------------------------------------------- | ||
| doses <- c(0, 2, 4, 6, 8) | ||
| models <- DoseFinding::Mods( | ||
| doses = doses, maxEff = 1.65, linear = NULL, emax = 0.79, sigEmax = c(4, 5) | ||
| ) | ||
| Delta <- 1.9 | ||
|
|
||
| obj <- new.env(parent = baseenv()) | ||
| code <- generate_setup_code( | ||
| doses, models, Delta, outcome_type = "continuous", | ||
| optimization_metric = "MAE", rl_models = models,seed = 123, alpha = 0.025, | ||
| selModel = "AIC", Delta_range = c(0.9, 1.1) * Delta) | ||
| eval(code, obj) | ||
|
|
||
| test_that("compute_reward_TD large Delta", { | ||
| act <- obj$compute_reward_TD(8, "linear") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(9, "linear") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(10, "linear") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(11, "linear") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(13, "emax") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(14, "emax") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(1000, "emax") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(1000, "sigEmax") | ||
| expect_equal(act, expected = 0) | ||
| }) | ||
|
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||
|
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||
| # direction = decreasing -------------------------------------------------- | ||
| doses <- c(0, 2, 4, 6, 8) | ||
| models <- DoseFinding::Mods( | ||
| doses = doses, maxEff = -1.65, linear = NULL, emax = 0.79, sigEmax = c(4, 5), | ||
| direction = "decreasing" | ||
| ) | ||
| Delta <- 1.3 | ||
|
|
||
| obj <- new.env(parent = baseenv()) | ||
| code <- generate_setup_code( | ||
| doses, models, Delta, outcome_type = "continuous", | ||
| optimization_metric = "MAE", rl_models = models,seed = 123, alpha = 0.025, | ||
| selModel = "AIC", Delta_range = c(0.9, 1.1) * Delta) | ||
| eval(code, obj) | ||
|
|
||
| test_that("compute_reward_TD direction = decreasing", { | ||
| act <- obj$compute_reward_TD(5, "linear") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(6, "linear") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(7, "linear") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(1, "emax") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(2, "emax") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(3, "emax") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(4, "sigEmax") | ||
| expect_equal(act, expected = 0) | ||
| act <- obj$compute_reward_TD(5, "sigEmax") | ||
| expect_equal(act, expected = 1) | ||
| act <- obj$compute_reward_TD(6, "sigEmax") | ||
| expect_equal(act, expected = 0) | ||
| }) | ||
|
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||
|
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||
| # outcome_type = binary --------------------------------------------------- | ||
| doses <- c(0, 0.5, 1.5, 2.5, 4) | ||
| models <- DoseFinding::Mods( | ||
| doses = doses, placEff = qlogis(0.1), maxEff = qlogis(0.35) - qlogis(0.1), | ||
| emax = c(0.25, 1), sigEmax = rbind(c(1, 3), c(2.5, 4)), betaMod = c(1.1, 1.1) | ||
| ) | ||
| Delta <- 1.4 | ||
|
|
||
| obj <- new.env() | ||
| code <- generate_setup_code( | ||
| doses, models, Delta, outcome_type = "binary", | ||
| optimization_metric = "MAE", rl_models = models, seed = 123, alpha = 0.05, | ||
| selModel = "AIC", Delta_range = c(0.9, 1.1) * Delta) | ||
| eval(code, obj) | ||
|
|
||
| test_that("compute_reward outcome_type = binary fit well", { | ||
| model_names <- c("emax1", "emax2", "sigEmax1", "sigEmax2", "betaMod") | ||
| optimization_metrics <- c("MAE", "power", "TD", "power and MAE") | ||
|
|
||
| df_expected <- data.frame( | ||
| emax1 = c(0.95762338, 1, 1, 0.95762338), | ||
| emax2 = c(0.97962054, 1, 1, 0.97962054), | ||
| sigEmax1 = c(0.92066444, 1, 1, 0.92066444), | ||
| sigEmax2 = c(0.89181074, 1, 1, 0.89181074), | ||
| betaMod = c(0.96190979, 1, 1, 0.96190979), | ||
| row.names = optimization_metrics | ||
| ) | ||
|
|
||
| for (model_name in model_names) { | ||
| set.seed(123) | ||
| sim_doses <- sample(doses, 1400, replace = TRUE) | ||
| actions <- seq_along(obj$doses) | ||
| names(actions) <- obj$doses | ||
| sim_actions <- actions[as.character(sim_doses)] | ||
| true_resps <- obj$true_response_list[[model_name]][sim_actions] | ||
| sim_resps <- rbinom(1400, 1, plogis(true_resps)) | ||
|
|
||
| for (optimization_metric in optimization_metrics) { | ||
| obj$optimization_metric <- optimization_metric | ||
| act <- obj$compute_reward(model_name, sim_doses, sim_resps) | ||
| expected <- df_expected[optimization_metric, model_name] | ||
| expect_equal(act, expected) | ||
| } | ||
| } | ||
|
|
||
| obj$optimization_metric <- "MAE" | ||
| }) | ||
|
|
||
| test_that("compute_reward outcome_type = binary don't fit well", { | ||
| model_names <- c("emax1", "emax2", "sigEmax1", "sigEmax2", "betaMod") | ||
| optimization_metrics <- c("MAE", "power", "TD", "power and MAE") | ||
|
|
||
| df_expected <- data.frame( | ||
| emax1 = c(-0.21123768, 0, 0, 0), | ||
| emax2 = c(-0.32752064, 0, 0, 0), | ||
| sigEmax1 = c(0.043401334, 0, 0, 0), | ||
| sigEmax2 = c(-0.89546023, 0, 0, 0), | ||
| betaMod = c(-0.52750171, 0, 0, 0), | ||
| row.names = optimization_metrics | ||
| ) | ||
|
|
||
| for (model_name in model_names) { | ||
| set.seed(123) | ||
| sim_doses <- sample(doses, 100, replace = TRUE) | ||
| actions <- seq_along(obj$doses) | ||
| names(actions) <- obj$doses | ||
| sim_actions <- actions[as.character(sim_doses)] | ||
| true_resps <- obj$true_response_list[[model_name]][sim_actions] | ||
| sim_resps <- rbinom(100, 1, plogis(true_resps)) | ||
|
|
||
| for (optimization_metric in optimization_metrics) { | ||
| obj$optimization_metric <- optimization_metric | ||
| act <- obj$compute_reward(model_name, sim_doses, sim_resps) | ||
| expected <- df_expected[optimization_metric, model_name] | ||
| expect_equal(act, expected) | ||
| } | ||
| } | ||
|
|
||
| obj$optimization_metric <- "MAE" | ||
| }) |