skip_connection("broom-pca")
skip_on_livy()
skip_on_arrow_devel()
skip_databricks_connect()
test_that("pca tidiers work", {
## ---------------- Connection and data upload to Spark ----------------------
sc <- testthat_spark_connection()
test_requires_version("2.0.0")
iris_tbl <- testthat_tbl("iris")
pca_model <- iris_tbl %>%
select(-Species) %>%
ml_pca(k = 3)
## ----------------------------- tidy() --------------------------------------
td1 <- tidy(pca_model)
model <- iris %>%
dplyr::select(-Species) %>%
stats::prcomp()
check_tidy(td1,
exp.row = 4, exp.col = 4,
exp.names = c("features", "PC1", "PC2", "PC3")
)
## --------------------------- augment() -------------------------------------
expect_equal(td1$PC1,
-as.vector(model$rotation[, 1]),
tolerance = 0.001, scale = 1
)
au1 <- pca_model %>%
augment(head(iris_tbl, 25)) %>%
collect()
check_tidy(au1,
exp.row = 25,
exp.name = c(
dplyr::tbl_vars(iris_tbl),
"PC1", "PC2", "PC3"
)
)
## ---------------------------- glance() -------------------------------------
gl1 <- glance(pca_model)
check_tidy(gl1,
exp.row = 1,
exp.names = c(
"k", "explained_variance_PC1",
"explained_variance_PC2", "explained_variance_PC3"
)
)
})
test_clear_cache()
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