skip_connection("ml-fpm-fpgrowth")
skip_on_livy()
skip_on_arrow_devel()
skip_databricks_connect()
test_that("ml_fpgrowth() default params", {
test_requires_version("3.0.0")
sc <- testthat_spark_connection()
test_default_args(sc, ml_fpgrowth)
})
test_that("ml_fpgrowth() param setting", {
test_requires_version("3.0.0")
sc <- testthat_spark_connection()
test_args <- list(
items_col = "wefwef",
min_confidence = 0.7,
min_support = 0.4,
prediction_col = "waef"
)
test_param_setting(sc, ml_fpgrowth, test_args)
})
test_that("ml_fpgrowth() works properly", {
sc <- testthat_spark_connection()
test_requires_version("2.2.0", "fpgrowth requires spark 2.2.0+")
df <- data.frame(items = c("1 2 5", "1 2 3 5", "1 2"))
df_tbl <- sdf_copy_to(sc, df, overwrite = TRUE) %>%
mutate(items = split(items, " "))
fp_model <- df_tbl %>%
ml_fpgrowth(min_support = 0.5, min_confidence = 0.6)
expect_equal(
ml_freq_itemsets(fp_model) %>%
sdf_nrow(),
7
)
expect_equal(
ml_association_rules(fp_model) %>%
sdf_nrow(),
9
)
expect_identical(
fp_model %>%
ml_transform(df_tbl) %>%
pull(prediction) %>%
lapply(as.list),
list(list(), list(), list("5"))
)
})
test_clear_cache()
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