Nothing
context("families")
test_that("tfd families", {
families = c("normal", "bernoulli", "bernoulli_prob", "beta", "betar",
"cauchy", "chi2", "chi",
"exponential", "gamma", "gammar",
"gumbel", "half_cauchy", "half_normal", "horseshoe",
"inverse_gamma", "inverse_gaussian", "laplace",
"log_normal", "logistic", "multinomial", "multinoulli", "negbinom",
"pareto_ls", "poisson", "poisson_lograte", "student_t", "student_t_ls",
"uniform",
"zip"
)
for (fam in families) {
d = make_tfd_dist(fam)
expect_is(d, "function")
np = attr(d, "nrparams_dist")
expect_true(np %in% c(1:3))
}
d = make_tfd_dist("zip", trafo_list = list(exp, exp))
expect_is(d, "function")
families = c("categorical",
"dirichlet_multinomial",
"dirichlet",
"gamma_gamma",
"geometric",
"kumaraswamy",
"truncated_normal",
"von_mises",
"von_mises_fisher",
"wishart",
"zipf",
"binomial"
)
for (fam in families) {
expect_error(make_tfd_dist(fam), "not implemented yet")
}
})
test_that("tfd families can be fitted", {
n <- 100
# FIXME: Currently not working:
# NA in fitted: "cauchy", "half_cauchy", "inverse_gamma", "student_t", "student_t_ls", "uniform"
dists = c(
"normal", "bernoulli", "bernoulli_prob",
"beta", "betar", "chi2", "chi","exponential",
"gamma", "gammar", "gumbel", "half_normal", "horseshoe",
"inverse_gaussian", "laplace", "log_normal",
"logistic", "negbinom", "negbinom",
"pareto_ls", "poisson", "poisson_lograte"
)
for(dist in dists) {
set.seed(24)
x <- runif(n) %>% as.matrix()
z <- runif(n) %>% as.matrix()
y <- exp(as.matrix(0.5*x + rnorm(n, 0, 0.1*z) + 1))
data = data.frame(x = x, z = z)
if (dist %in% c("beta", "betar")) {
y <- (y - min(y)) / (max(y) + 0.01 - min(y)) + runif(n, 1e-5, 1e-4)
}
suppressWarnings(
mod <- deepregression(
y = y,
data = data,
# define how parameters should be modeled
list_of_formulas = list(~ 1 + x, ~ 1 + z, ~ 1),
list_of_deep_models = NULL,
family = dist, tf_seed = 44,
optimizer = tf$keras$optimizers$RMSprop(learning_rate = 0.000001)
)
)
cat("Fitting", dist, "\n")
res <- mod %>% fit(epochs=2L, verbose = FALSE, view_metrics = FALSE)
expect_true(!sum(is.nan(unlist(res$metrics))) > 0)
expect_true(!any(unlist(res$metrics)==Inf))
expect_is(mod, "deepregression")
expect_true(!any(is.nan(unlist(coef(mod)))))
expect_true(!any(is.nan(fitted(mod))))
suppressWarnings(res <- mod %>% predict(data))
expect_true(is.numeric(res))
expect_true(!any(is.nan(res)))
}
})
test_that("tfd_zip", {
zipfun = tfd_zip(probs=c(0.1, 0.9), lambda=2)
expect_is(zipfun, "python.builtin.object")
expect_is(zipfun$cdf, "python.builtin.method")
expect_true(as.numeric(zipfun$log_prob(1)) < 0)
expect_true(as.numeric(zipfun$log_prob(0)) > as.numeric(zipfun$log_prob(1)))
})
test_that("tffuns", {
x = 2
y = 3
expect_is(tfe(x), "tensorflow.tensor")
expect_is(tfsig(x), "tensorflow.tensor")
expect_is(tfsoft(c(x,y)), "tensorflow.tensor")
expect_is(tfsqrt(x), "tensorflow.tensor")
expect_is(tfsq(x), "tensorflow.tensor")
expect_is(tfdiv(x,y), c("numeric","tensorflow.tensor"))
expect_is(tfrec(x), "tensorflow.tensor")
expect_is(tfmult(x,y), "tensorflow.tensor")
})
test_that("tfd_mvr", {
n <- 100
set.seed(24)
x <- runif(n) %>% as.matrix()
z <- runif(n) %>% as.matrix()
y <- exp(as.matrix(0.5*x + rnorm(n, 0, 0.1*z) + 1))
data = data.frame(x = x, z = z)
suppressWarnings(
mod <- deepregression(
y = y,
data = data,
# define how parameters should be modeled
list_of_formulas = list(~ 1 + x, ~ 1 + x),
list_of_deep_models = NULL,
family = "mvr",
tf_seed = 44
)
)
res <- mod %>% fit(epochs=2L, verbose = FALSE, view_metrics = FALSE)
expect_true(!sum(is.nan(unlist(res$metrics))) > 0)
expect_true(!any(unlist(res$metrics)==Inf))
expect_is(mod, "deepregression")
expect_true(!any(is.nan(unlist(coef(mod)))))
expect_true(!any(is.nan(fitted(mod))))
suppressWarnings(res <- mod %>% predict(data))
expect_true(is.numeric(res))
expect_true(!any(is.nan(res)))
})
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