context("fitted-vb")
set_cmdstan_path()
fit_vb <- testing_fit("logistic", method = "variational", seed = 123)
fit_vb_sci_not <- testing_fit("logistic", method = "variational", seed = 123, iter = 200000, adapt_iter = 100000)
mod <- testing_model("bernoulli")
data_list <- testing_data("bernoulli")
PARAM_NAMES <- c("alpha", "beta[1]", "beta[2]", "beta[3]")
test_that("summary() method works after vb", {
x <- fit_vb$summary()
expect_s3_class(x, "draws_summary")
expect_equal(x$variable, c("lp__", "lp_approx__", PARAM_NAMES))
x <- fit_vb$summary(variables = NULL, c("mean", "sd"))
expect_s3_class(x, "draws_summary")
expect_equal(x$variable, c("lp__", "lp_approx__", PARAM_NAMES))
expect_equal(colnames(x), c("variable", "mean", "sd"))
})
test_that("print() method works after vb", {
expect_output(expect_s3_class(fit_vb$print(), "CmdStanVB"), "variable")
expect_output(fit_vb$print(max_rows = 1), "# showing 1 of 6 rows")
# test on model with more parameters
fit <- cmdstanr_example("schools_ncp", method = "variational", seed = 123)
expect_output(fit$print(), "lp_approx__")
expect_output(fit$print(), "showing 10 of 20 rows")
expect_output(fit$print(max_rows = 20), "theta[8]", fixed = TRUE) # last parameter
expect_error(
fit$print(variable = "unknown", max_rows = 20),
"Can't find the following variable(s): unknown",
fixed = TRUE
) # unknown parameter
out <- capture.output(fit$print(c("theta", "tau", "lp__", "lp_approx__")))
expect_length(out, 13) # columns names + 8 thetas + tau + lp__ + lp_approx__ + empty + message
expect_match(out[1], " variable")
expect_match(out[2], " theta[1]", fixed = TRUE)
expect_match(out[9], " theta[8]", fixed = TRUE)
expect_match(out[10], " tau")
expect_match(out[11], " lp__")
expect_false(nzchar(out[12])) # empty line
expect_match(out[13], "10 of 11 rows")
})
test_that("draws() method returns posterior sample (reading csv works)", {
draws <- fit_vb$draws()
expect_type(draws, "double")
expect_s3_class(draws, "draws_matrix")
expect_equal(posterior::variables(draws), c("lp__", "lp_approx__", PARAM_NAMES))
})
test_that("lp(), lp_approx() methods return vectors (reading csv works)", {
lp <- fit_vb$lp()
lg <- fit_vb$lp_approx()
expect_type(lp, "double")
expect_type(lg, "double")
expect_equal(length(lp), nrow(fit_vb$draws()))
expect_equal(length(lg), length(lp))
})
test_that("vb works with scientific notation args", {
x <- fit_vb_sci_not$summary()
expect_s3_class(x, "draws_summary")
expect_equal(x$variable, c("lp__", "lp_approx__", PARAM_NAMES))
x <- fit_vb_sci_not$summary(variables = NULL, c("mean", "sd"))
expect_s3_class(x, "draws_summary")
expect_equal(x$variable, c("lp__", "lp_approx__", PARAM_NAMES))
expect_equal(colnames(x), c("variable", "mean", "sd"))
})
test_that("time() method works after vb", {
run_times <- fit_vb$time()
checkmate::expect_list(run_times, names = "strict", any.missing = FALSE)
testthat::expect_named(run_times, c("total"))
checkmate::expect_number(run_times$total, finite = TRUE)
})
test_that("output() works for vb", {
expect_output(fit_vb$output(),
"method = variational")
})
test_that("time is reported after vb", {
expect_output(
mod$variational(data = data_list,
seed = 123,
elbo_samples = 1000,
iter = 2000,
output_samples = 50
),
"Finished in"
)
})
test_that("draws() works for different formats", {
a <- fit_vb$draws()
expect_true(posterior::is_draws_matrix(a))
a <- fit_vb$draws(format = "list")
expect_true(posterior::is_draws_list(a))
a <- fit_vb$draws(format = "array")
expect_true(posterior::is_draws_array(a))
a <- fit_vb$draws(format = "df")
expect_true(posterior::is_draws_df(a))
})
test_that("draws() errors if invalid format", {
expect_error(
fit_vb$draws(format = "bad_format"),
"The supplied draws format is not valid"
)
})
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