Feature Transformation – PCA (Estimator)

R/ml_feature_pca.R

ft_pca

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", ...)

Arguments

Arguments Description
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, 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.

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

Examples

library(sparklyr)
 
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) 
#> Explained variance:
#> 
#>        PC1        PC2 
#> 0.92461872 0.05306648 
#> 
#> Rotation:
#>                      PC1         PC2
#> Sepal_Length -0.36138659 -0.65658877
#> Sepal_Width   0.08452251 -0.73016143
#> Petal_Length -0.85667061  0.17337266
#> Petal_Width  -0.35828920  0.07548102

See Also

See https://spark.apache.org/docs/latest/ml-features.html for more information on the set of transformations available for DataFrame columns in 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_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()