Feature Tranformation -- 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, output_col, k, dataset = 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

dataset

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

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

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

See http://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_count_vectorizer, ft_dct, ft_elementwise_product, ft_hashing_tf, ft_idf, ft_index_to_string, ft_ngram, ft_one_hot_encoder, ft_quantile_discretizer, ft_r_formula, ft_regex_tokenizer, ft_sql_transformer, ft_stop_words_remover, ft_string_indexer, ft_tokenizer, ft_vector_assembler, ft_word2vec