Nothing
context("deepregression methods")
test_that("all methods", {
n <- 1500
deep_model <- function(x) x %>%
layer_dense(units = 2L, activation = "relu", use_bias = FALSE) %>%
layer_dense(units = 1L, activation = "linear")
x <- runif(n) %>% matrix(ncol=3)
true_mean_fun <- function(xx) sin(10 * apply(xx, 1, mean) + 1) +
sapply(xx[, 3], function(x) rnorm(1, mean=0,sd=x))
data = data.frame(matrix(x, ncol=3))
y <- true_mean_fun(data)
mod <- deepregression(
y = y,
data = data,
list_of_formulas = list(loc = ~ X3 + d(X1) + g(X2), scale = ~X3),
list_of_deep_models = list(d = deep_model, g = deep_model)
)
mod %>% fit(epochs=3L, verbose = FALSE, view_metrics = TRUE)
mn = mean(mod, data)
expect_is(mn, "matrix")
expect_true(nrow(mn) == 500)
expect_true(length(unique(mn)) > 1L)
std = stddev(mod, data)
expect_is(std, "matrix")
expect_true(nrow(std) == 500)
expect_true(length(unique(std)) > 1L)
q95 = quant(mod, data, 0.95)
q05 = quant(mod, data, 0.05)
expect_true(all(q95 > mn & mn > q05))
expect_equal(predict(mod, data), mn)
expect_equal(fitted(mod), mn)
expect_true(plot(mod) == "No smooth effects. Nothing to plot.")
cf = coef(mod)
expect_is(cf, "list")
# lapply(cf, function(x) {
# sl = x$structured_linear
# expect_is(sl, "matrix")
# expect_equal(dim(sl), c(2,1))
# NULL
# })
expect_output(print(mod), "Model")
expect_output(print(mod), "Total params: 14")
dst = get_distribution(mod)
expect_is(dst, "python.builtin.object")
ls = log_score(mod, data)
expect_true(all(ls < 0))
expect_true(all(dim(ls) == c(500, 1)))
})
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