Feature Transformation -- PCA (Estimator)

PCA trains a model to project vectors to a lower dimensional space of the top k principal components.

ft_pca(x, input_col = NULL, output_col = NULL, k = NULL,
  uid = random_string("pca_"), ...)

ml_pca(x, features = tbl_vars(x), k = length(features),
  pc_prefix = "PC", ...)

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.

k

The number of principal components

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

features

The columns to use in the principal components analysis. Defaults to all columns in x.

pc_prefix

Length-one character vector used to prepend names of components.

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

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.

ml_pca() is a wrapper around ft_pca() that returns a ml_model.

See also

Examples

if (FALSE) { library(dplyr) sc <- spark_connect(master = "local") iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) iris_tbl %>% select(-Species) %>% ml_pca(k = 2) }