ft_vector_slicer: Feature Transformation - VectorSlicer (Transformer)

Description Usage Arguments Value See Also

View source: R/ml_feature_vector_slicer.R

Description

Takes a feature vector and outputs a new feature vector with a subarray of the original features.

Usage

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ft_vector_slicer(
  x,
  input_col = NULL,
  output_col = NULL,
  indices = NULL,
  uid = random_string("vector_slicer_"),
  ...
)

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.

indices

An vector of indices to select features from a vector column. Note that the indices are 0-based.

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

Value

The object returned depends on the class of x.

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_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_pca(), 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_word2vec()


sparklyr documentation built on June 17, 2021, 5:06 p.m.