skip_connection("broom-aft_survival_regression")
skip_on_livy()
skip_on_arrow_devel()
skip_databricks_connect()
test_that("aft_survival_regression.tidy() works", {
## ---------------- Connection and data upload to Spark ----------------------
sc <- testthat_spark_connection()
test_requires_version("2.0.0")
df <- data.frame(
label = c(1.218, 2.949, 3.627, 0.273, 4.199),
censor = c(1, 0, 0, 1, 0),
a = c(1.560, 0.346, 1.380, 0.520, 0.795),
b = c(-0.605, 2.158, 0.231, 1.151, -0.226)
)
df_tbl <- sdf_copy_to(sc, df, name = "df_tbl", overwrite = TRUE)
aft_model <- df_tbl %>%
ml_aft_survival_regression(label ~ a + b, censor = "censor")
## ----------------------------- tidy() --------------------------------------
td1 <- tidy(aft_model)
check_tidy(td1,
exp.row = 3, exp.col = 2,
exp.names = c("features", "coefficients")
)
expect_equal(td1$coefficients, c(2.64, -0.496, 0.198),
tolerance = 0.001, scale = 1
)
## --------------------------- augment() -------------------------------------
au1 <- augment(aft_model) %>%
collect()
check_tidy(au1,
exp.row = 5,
exp.name = c(
dplyr::tbl_vars(df_tbl),
".prediction"
)
)
## ---------------------------- glance() -------------------------------------
gl1 <- glance(aft_model)
check_tidy(gl1,
exp.row = 1,
exp.names = c("scale", "aggregation_depth")
)
})
test_clear_cache()
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