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,
  dataset = NULL, uid = random_string("pca_"), ...)

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



A spark_connection, ml_pipeline, or a tbl_spark.


The name of the input column.


The name of the output column.


The number of principal components


(Optional) A tbl_spark. If provided, eagerly fit the (estimator) feature "transformer" against dataset. See details.


A character string used to uniquely identify the feature transformer.


Optional arguments; currently unused.


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


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


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


When dataset is provided for an estimator transformer, the function internally calls ml_fit() against dataset. Hence, the methods for spark_connection and ml_pipeline will then return a ml_transformer and a ml_pipeline with a ml_transformer appended, respectively. When x is a tbl_spark, the estimator will be fit against dataset before transforming x.

When dataset is not specified, the constructor returns a ml_estimator, and, in the case where x is a tbl_spark, the estimator fits against x then to obtain a transformer, which is then immediately used to transform x.

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

See also



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)
# }