Feature Transformation - PCA (Estimator)
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, 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. 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.
See Also
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(), ft_one_hot_encoder_estimator(), 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
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)