ft_standard_scaler {sparklyr}R Documentation

Feature Transformation – StandardScaler (Estimator)

Description

Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set. The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.

Usage

ft_standard_scaler(x, input_col = NULL, output_col = NULL,
  with_mean = FALSE, with_std = TRUE,
  uid = random_string("standard_scaler_"), ...)

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.

with_mean

Whether to center the data with mean before scaling. It will build a dense output, so take care when applying to sparse input. Default: FALSE

with_std

Whether to scale the data to unit standard deviation. Default: TRUE

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

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.

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, ft_pca, ft_polynomial_expansion, 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_vector_indexer, ft_vector_slicer, ft_word2vec

Examples

## Not run: 
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

features <- c("Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width")

iris_tbl %>%
  ft_vector_assembler(input_col = features,
                      output_col = "features_temp") %>%
  ft_standard_scaler(input_col = "features_temp",
                     output_col = "features",
                     with_mean = TRUE)

## End(Not run)


[Package sparklyr version 1.0.0 Index]