ft_standard_scaler: Feature Transformation -- StandardScaler (Estimator)

Description

Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set. The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.

Usage

ft_standard_scaler(
  x,
  input_col = NULL,
  output_col = NULL,
  with_mean = FALSE,
  with_std = TRUE,
  uid = random_string("standard_scaler_"),
  ...
)

Value

The object returned depends on the class of x.

spark_connection: When x is a spark_connection, the function returns a ml_transformer, a ml_estimator, or one of their subclasses. The object contains a pointer to a Spark Transformer or Estimator object and can be used to compose Pipeline objects.
ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the transformer or estimator appended to the pipeline.
tbl_spark: When x is a tbl_spark, a transformer is constructed then immediately applied to the input tbl_spark, returning a tbl_spark

Arguments

x: A spark_connection, ml_pipeline, or a tbl_spark.
input_col: The name of the input column.
output_col: The name of the output column.
with_mean: Whether to center the data with mean before scaling. It will build a dense output, so take care when applying to sparse input. Default: FALSE
with_std: Whether to scale the data to unit standard deviation. Default: TRUE
uid: A character string used to uniquely identify the feature transformer.
...: Optional arguments; currently unused.

Details

In the case where x is a tbl_spark, the estimator fits against x to obtain a transformer, which is then immediately used to transform x, returning a tbl_spark.

Other feature transformers: ft_binarizer(), ft_bucketizer(), ft_chisq_selector(), ft_count_vectorizer(), ft_dct(), ft_elementwise_product(), ft_feature_hasher(), ft_hashing_tf(), ft_idf(), ft_imputer(), ft_index_to_string(), ft_interaction(), ft_lsh, ft_max_abs_scaler(), ft_min_max_scaler(), ft_ngram(), ft_normalizer(), ft_one_hot_encoder_estimator(), ft_one_hot_encoder(), ft_pca(), ft_polynomial_expansion(), ft_quantile_discretizer(), ft_r_formula(), ft_regex_tokenizer(), ft_robust_scaler(), ft_sql_transformer(), ft_stop_words_remover(), ft_string_indexer(), ft_tokenizer(), ft_vector_assembler(), ft_vector_indexer(), ft_vector_slicer(), ft_word2vec()

Examples

Run this code

if (FALSE) {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

features <- c("Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width")

iris_tbl %>%
  ft_vector_assembler(
    input_col = features,
    output_col = "features_temp"
  ) %>%
  ft_standard_scaler(
    input_col = "features_temp",
    output_col = "features",
    with_mean = TRUE
  )
}