ft_pca: Feature Transformation -- PCA (Estimator)

Description

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

Usage

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", ...)

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator or a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the transformer or estimator appended to it. If a tbl_spark, it will return a tbl_spark with the transformation applied to it.

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.

Details

In the case where x is a tbl_spark, the estimator fits against x to obtain a transformer, returning a tbl_spark.

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

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_polynomial_expansion(), ft_quantile_discretizer(), ft_r_formula(), ft_regex_tokenizer(), ft_robust_scaler(), ft_sql_transformer(), ft_standard_scaler(), 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) {
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)
}