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test_calculate.R
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test_calculate.R
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context("calculate")
test_that("deterministic calculate works with correct lists", {
skip_if_not(check_tf_version())
source("helpers.R")
# unknown variable
x <- as_data(c(1, 2))
a <- normal(0, 1)
y <- a * x
vals <- calculate(y, values = list(a = 3))
expect_equal(vals$y, matrix(c(3, 6)))
# unknown variable and new data
x <- as_data(c(1, 2))
a <- normal(0, 1)
y <- a * x
vals <- calculate(y, values = list(a = 6, x = c(2, 1)))
expect_equal(vals$y, matrix(c(12, 6)))
# fixed value depending on multiple variables
x <- as_data(c(1, 2))
a1 <- normal(0, 1)
a2 <- normal(0, 1, truncation = c(0, Inf))
a <- a1 * a2
y <- a * x
vals <- calculate(y, values = list(a = 6, x = c(2, 1)))
expect_equal(vals$y, matrix(c(12, 6)))
})
test_that("stochastic calculate works with correct lists", {
skip_if_not(check_tf_version())
source("helpers.R")
# nolint start
# with y ~ N(100, 1 ^ 2), it should be very unlikely that y <= 90
# ( pnorm(90, 100, 1) = 7e-24 )
# nolint end
nsim <- 97
# fix variable
a <- normal(0, 1)
y <- normal(a, 1)
sims <- calculate(y, nsim = nsim, values = list(a = 100))
expect_true(all(sims$y > 90))
expect_equal(dim(sims$y), c(nsim, dim(y)))
# fix variable with more dims on y
a <- normal(0, 1)
y <- normal(a, 1, dim = c(3, 3, 3))
sims <- calculate(y, nsim = nsim, values = list(a = 100))
expect_true(all(sims$y > 90))
expect_equal(dim(sims$y), c(nsim, dim(y)))
# fix variable and new data
x <- as_data(1)
a <- normal(0, 1)
y <- normal(a * x, 1)
sims <- calculate(y, nsim = nsim, values = list(a = 50, x = 2))
expect_true(all(sims$y > 90))
expect_equal(dim(sims$y), c(nsim, dim(y)))
# data with distribution
x <- as_data(1)
y <- as_data(randn(10))
a <- normal(0, 1)
distribution(y) <- normal(a * x, 1)
sims <- calculate(y, nsim = nsim, values = list(a = 50, x = 2))
expect_true(all(sims$y > 90))
expect_equal(dim(sims$y), c(nsim, dim(y)))
# multivariate data with distribution
n <- 10
k <- 3
x <- ones(1, k)
y <- as_data(randn(n, k))
a <- normal(0, 1)
distribution(y) <- multivariate_normal(a * x, diag(k), n_realisations = n)
sims <- calculate(y, nsim = nsim, values = list(a = 50, x = rep(2, k)))
expect_true(all(sims$y > 90))
expect_equal(dim(sims$y), c(nsim, dim(y)))
# weird multivariate data with distribution
n <- 10
k <- 3
x <- ones(1, k)
y <- matrix(0, n, k)
idx <- sample.int(k, n, replace = TRUE)
y[cbind(seq_len(n), idx)] <- 1
y <- as_data(y)
a <- normal(0, 1, dim = c(1, k))
distribution(y) <- categorical(ilogit(a * x), n_realisations = n)
sims <- calculate(y,
nsim = nsim,
values = list(a = c(50, 5, 0.5),
x = rep(2, k)))
expect_true(all(apply(sims$y, 1:2, sum) == 1))
expect_equal(dim(sims$y), c(nsim, dim(y)))
})
test_that("deterministic calculate works with greta_mcmc_list objects", {
skip_if_not(check_tf_version())
source("helpers.R")
samples <- 10
x <- as_data(c(1, 2))
a <- normal(0, 1)
y <- a * x
m <- model(y)
draws <- mcmc(m, warmup = 0, n_samples = samples, verbose = FALSE)
# with an existing greta array
y_values <- calculate(y, values = draws)
# correct class
expect_s3_class(y_values, "greta_mcmc_list")
# correct dimensions
expect_equal(dim(y_values[[1]]), c(10, 2))
# all valid values
expect_true(all(is.finite(as.vector(y_values[[1]]))))
# with a new greta array, based on a different element in the model
new_values <- calculate(a ^ 2, values = draws)
# correct class
expect_s3_class(new_values, "greta_mcmc_list")
# correct dimensions
expect_equal(dim(new_values[[1]]), c(10, 1))
# all valid values
expect_true(all(is.finite(as.vector(new_values[[1]]))))
})
test_that("calculate with greta_mcmc_list doesn't mix up variables", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- normal(-100, 0.001)
b <- normal(100, 0.001)
c <- normal(0, 0.001)
result <- b * c + a
model <- model(a, b)
draws <- mcmc(model, warmup = 100, n_samples = 100, verbose = FALSE)
result_draws <- calculate(result, values = draws)
vals <- as.vector(as.matrix(result_draws))
# the values should be around -100 if the variables aren't mixed up, or a long
# way off if they are
expect_gt(min(vals), -105)
expect_lt(max(vals), -95)
})
test_that("calculate with greta_mcmc_list doesn't lose track of new nodes", {
skip_if_not(check_tf_version())
source("helpers.R")
z <- normal(0, 1)
m <- model(z)
draws <- mcmc(m, warmup = 100, n_samples = 100, verbose = FALSE)
x <- z ^ 2
expect_ok(x_draws <- calculate(x, values = draws))
expect_equal(as.matrix(x_draws)[, 1], as.matrix(draws)[, 1] ^ 2)
y <- z * 2
expect_ok(y_draws <- calculate(y, values = draws))
expect_equal(as.matrix(y_draws)[, 1], as.matrix(draws)[, 1] * 2)
})
test_that("stochastic calculate works with greta_mcmc_list objects", {
skip_if_not(check_tf_version())
source("helpers.R")
samples <- 10
chains <- 2
n <- 100
y <- as_data(rnorm(n))
x <- as_data(1)
a <- normal(0, 1)
distribution(y) <- normal(a, x)
m <- model(a)
draws <- mcmc(
m,
warmup = 0,
n_samples = samples,
chains = chains,
verbose = FALSE
)
# this should error without nsim being specified (y is stochastic)
expect_error(calculate(a, y, values = draws),
"y has a distribution and is not in the MCMC samples")
# this should be OK
sims <- calculate(y, values = draws, nsim = 10)
expect_equal(dim(sims$y), c(10, dim(y)))
# for a list of targets, the result should be a list
nsim <- 10
sims <- calculate(a, y, values = draws, nsim = nsim)
# correct class, dimensions, and valid values
expect_true(is.list(sims))
expect_equal(names(sims), c("a", "y"))
expect_equal(dim(sims$a), c(nsim, 1, 1))
expect_equal(dim(sims$y), c(nsim, n, 1))
expect_true(all(is.finite(sims$a)) & all(is.finite(sims$y)))
# a single array with these nsim observations
sims <- calculate(y, values = draws, nsim = nsim)
expect_true(is.numeric(sims$y))
expect_equal(dim(sims$y), c(nsim, n, 1))
expect_true(all(is.finite(sims$y)))
# warn about resampling if nsim is greater than elements in draws
expect_warning(calculate(y, values = draws, nsim = samples * chains + 1),
"posterior samples had to be drawn with replacement")
})
test_that("calculate errors if the mcmc samples unrelated to target", {
skip_if_not(check_tf_version())
source("helpers.R")
samples <- 10
chains <- 2
n <- 100
y <- as_data(rnorm(n))
x <- as_data(1)
a <- normal(0, 1)
distribution(y) <- normal(a, x)
m <- model(a)
draws <- mcmc(
m,
warmup = 0,
n_samples = samples,
chains = chains,
verbose = FALSE
)
c <- normal(0, 1)
expect_error(calculate(c, values = draws),
"do not appear to be connected")
})
test_that("stochastic calculate works with mcmc samples & new stochastics", {
skip_if_not(check_tf_version())
source("helpers.R")
samples <- 10
chains <- 2
n <- 100
y <- as_data(rnorm(n))
x <- as_data(1)
a <- normal(0, 1)
distribution(y) <- normal(a, x)
m <- model(a)
draws <- mcmc(
m,
warmup = 0,
n_samples = samples,
chains = chains,
verbose = FALSE
)
# new stochastic greta array
b <- lognormal(a, 1)
# this should error without nsim being specified (b is stochastic and not
# given by draws)
expect_error(calculate(b, values = draws),
"new variables that are not in the MCMC samples")
sims <- calculate(b, values = draws, nsim = 10)
expect_equal(dim(sims$b), c(10, dim(b)))
expect_true(all(sims$b > 0))
})
test_that("calculate errors nicely if non-greta arrays are passed", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- c(1, 2)
a <- normal(0, 1)
y <- a * x
# it should error nicely
expect_error(calculate(y, x, values = list(x = c(2, 1))),
regexp = "not greta arrays: x")
# and a hint for this common error
expect_error(calculate(y, list(x = c(2, 1))),
"Perhaps you forgot to explicitly name other arguments?")
})
test_that("calculate errors nicely if values for stochastics not passed", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- as_data(c(1, 2))
a <- normal(0, 1)
y <- a * x
# it should error nicely
expect_error(calculate(y, values = list(x = c(2, 1))),
paste("values have not been provided for all greta arrays on",
"which the target depends, and nsim has not been set.",
"Please provide values for the greta array: a"))
# but is should work fine if nsim is set
expect_ok(calculate(y, values = list(x = c(2, 1)), nsim = 1))
})
test_that("calculate errors nicely if values have incorrect dimensions", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- as_data(c(1, 2))
a <- normal(0, 1)
y <- a * x
# it should error nicely
expect_error(calculate(y, values = list(a = c(1, 1))),
"different number of elements than the greta array")
})
test_that("calculate works with variable batch sizes", {
skip_if_not(check_tf_version())
source("helpers.R")
samples <- 100
x <- as_data(c(1, 2))
a <- normal(0, 1)
y <- a * x
m <- model(y)
draws <- mcmc(m, warmup = 0, n_samples = samples, verbose = FALSE)
# variable valid batch sizes
val_1 <- calculate(y, values = draws, trace_batch_size = 1)
val_10 <- calculate(y, values = draws, trace_batch_size = 10)
val_100 <- calculate(y, values = draws, trace_batch_size = 100)
val_inf <- calculate(y, values = draws, trace_batch_size = Inf)
# check the first one
expect_s3_class(val_1, "greta_mcmc_list")
expect_equal(dim(val_1[[1]]), c(100, 2))
expect_true(all(is.finite(as.vector(val_1[[1]]))))
# check the others are the same
expect_identical(val_10, val_1)
expect_identical(val_100, val_1)
expect_identical(val_inf, val_1)
})
test_that("calculate errors nicely with invalid batch sizes", {
skip_if_not(check_tf_version())
source("helpers.R")
samples <- 100
x <- as_data(c(1, 2))
a <- normal(0, 1)
y <- a * x
m <- model(y)
draws <- mcmc(m, warmup = 0, n_samples = samples, verbose = FALSE)
# variable valid batch sizes
expect_error(calculate(y, values = draws, trace_batch_size = 0),
"greater than or equal to 1")
expect_error(calculate(y, values = draws, trace_batch_size = NULL),
"greater than or equal to 1")
expect_error(calculate(y, values = draws, trace_batch_size = NA),
"greater than or equal to 1")
})
test_that("calculate returns a named list", {
skip_if_not(check_tf_version())
source("helpers.R")
a <- as_data(randn(3))
b <- a ^ 2
c <- sqrt(b)
# if target is a single greta array, the output should be a single numeric
result <- calculate(b, nsim = 10)
expect_true(is.list(result))
expect_true(is.numeric(result$b))
# if target is a list, the output should be a list of numerics
result <- calculate(b, c, nsim = 10)
expect_true(is.list(result))
# check contents
are_numeric <- vapply(result, is.numeric, FUN.VALUE = logical(1))
expect_true(all(are_numeric))
# check names
expect_equal(names(result), c("b", "c"))
})
test_that("calculate produces the right number of samples", {
skip_if_not(check_tf_version())
source("helpers.R")
# fix variable
a <- normal(0, 1)
y <- normal(a, 1, dim = c(1, 3))
# should be vectors
sims <- calculate(a, nsim = 1)
expect_equal(dim(sims$a), c(1, dim(a)))
sims <- calculate(a, nsim = 17)
expect_equal(dim(sims$a), c(17, dim(a)))
sims <- calculate(y, nsim = 1)
expect_equal(dim(sims$y), c(1, dim(y)))
sims <- calculate(y, nsim = 19)
expect_equal(dim(sims$y), c(19, dim(y)))
# the global RNG seed should not change if the seed *is* specified
before <- rng_seed()
sims <- calculate(y, nsim = 1, seed = 12345)
after <- rng_seed()
expect_identical(before, after)
# the samples should differ if the seed is *not* specified
one <- calculate(y, nsim = 1)
two <- calculate(y, nsim = 1)
expect_false(identical(one, two))
# the samples should differ if the seeds are specified differently
one <- calculate(y, nsim = 1, seed = 12345)
two <- calculate(y, nsim = 1, seed = 54321)
expect_false(identical(one, two))
# the samples should be the same if the seed is the same
one <- calculate(y, nsim = 1, seed = 12345)
two <- calculate(y, nsim = 1, seed = 12345)
expect_identical(one, two)
})
test_that("calculate uses the local RNG seed", {
skip_if_not(check_tf_version())
source("helpers.R")
# fix variable
a <- normal(0, 1)
y <- normal(a, 1)
# the global RNG seed should change if the seed is *not* specified
before <- rng_seed()
sims <- calculate(y, nsim = 1)
after <- rng_seed()
expect_false(identical(before, after))
# the global RNG seed should not change if the seed *is* specified
before <- rng_seed()
sims <- calculate(y, nsim = 1, seed = 12345)
after <- rng_seed()
expect_identical(before, after)
# the samples should differ if the seed is *not* specified
one <- calculate(y, nsim = 1)
two <- calculate(y, nsim = 1)
expect_false(identical(one, two))
# the samples should differ if the seeds are specified differently
one <- calculate(y, nsim = 1, seed = 12345)
two <- calculate(y, nsim = 1, seed = 54321)
expect_false(identical(one, two))
# the samples should be the same if the seed is the same
one <- calculate(y, nsim = 1, seed = 12345)
two <- calculate(y, nsim = 1, seed = 12345)
expect_identical(one, two)
})
test_that("calculate works if distribution-free variables are fixed", {
skip_if_not(check_tf_version())
source("helpers.R")
# fix variable
a <- variable()
y <- normal(a, 1)
sims <- calculate(a, y, nsim = 1, values = list(a = 100))
expect_true(all(sims$y > 90))
})
test_that("calculate errors if distribution-free variables are not fixed", {
skip_if_not(check_tf_version())
source("helpers.R")
# fix variable
a <- variable()
y <- normal(a, 1)
expect_error(calculate(a, y, nsim = 1),
"do not have distributions so cannot be sampled")
})
test_that("calculate errors if a distribution cannot be sampled from", {
skip_if_not(check_tf_version())
source("helpers.R")
# fix variable
y <- hypergeometric(5, 3, 2)
expect_error(sims <- calculate(y, nsim = 1),
"sampling is not yet implemented")
})
test_that("calculate errors nicely if nsim is invalid", {
skip_if_not(check_tf_version())
source("helpers.R")
x <- normal(0, 1)
expect_error(calculate(x, nsim = 0),
"must be a positive integer")
expect_error(calculate(x, nsim = -1),
"must be a positive integer")
expect_error(calculate(x, nsim = "five"),
"must be a positive integer")
})