skip_connection("ml-feature-standard-scaler")
skip_on_livy()
skip_on_arrow_devel()
test_that("ft_standard_scaler() default params", {
test_requires_version("3.0.0")
sc <- testthat_spark_connection()
test_default_args(sc, ft_standard_scaler)
})
test_that("ft_standard_scaler() param setting", {
test_requires_version("3.0.0")
sc <- testthat_spark_connection()
test_args <- list(
input_col = "foo",
output_col = "bar",
with_mean = TRUE,
with_std = TRUE
)
test_param_setting(sc, ft_standard_scaler, test_args)
})
test_that("ft_standard_scaler() works properly", {
sc <- testthat_spark_connection()
sample_data_path <- get_test_data_path("sample_libsvm_data.txt")
sample_data <- spark_read_libsvm(sc, "sample_data",
sample_data_path,
overwrite = TRUE
)
scaler <- ft_standard_scaler(
sc,
input_col = "features", output_col = "scaledFeatures",
with_std = TRUE, with_mean = FALSE, uid = "standard_scalaer_999"
)
scaler_model <- ml_fit(scaler, sample_data)
expect_warning_on_arrow(
s_m <-scaler_model %>%
ml_transform(sample_data) %>%
head(1) %>%
dplyr::pull(scaledFeatures) %>%
unlist() %>%
sum()
)
expect_equal(
s_m,
295.3425,
tolerance = 0.001, scale = 1
)
expect_output_file(
print(scaler_model),
output_file("print/standard-scaler-model.txt")
)
})
test_clear_cache()
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