Feature Transformation – MinMaxScaler (Estimator)

R/ml_feature_min_max_scaler.R

ft_min_max_scaler

Description

Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling

Usage

 
ft_min_max_scaler( 
  x, 
  input_col = NULL, 
  output_col = NULL, 
  min = 0, 
  max = 1, 
  uid = random_string("min_max_scaler_"), 
  ... 
) 

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.
min	Lower bound after transformation, shared by all features Default: 0.0
max	Upper bound after transformation, shared by all features Default: 1.0
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.

• 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)
 
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_min_max_scaler( 
    input_col = "features_temp", 
    output_col = "features" 
  ) 
#> # Source: spark<?> [?? x 7]
#>    Sepal_L…¹ Sepal…² Petal…³ Petal…⁴ Species featu…⁵ featu…⁶
#>        <dbl>   <dbl>   <dbl>   <dbl> <chr>   <list>  <list> 
#>  1       5.1     3.5     1.4     0.2 setosa  <dbl>   <dbl>  
#>  2       4.9     3       1.4     0.2 setosa  <dbl>   <dbl>  
#>  3       4.7     3.2     1.3     0.2 setosa  <dbl>   <dbl>  
#>  4       4.6     3.1     1.5     0.2 setosa  <dbl>   <dbl>  
#>  5       5       3.6     1.4     0.2 setosa  <dbl>   <dbl>  
#>  6       5.4     3.9     1.7     0.4 setosa  <dbl>   <dbl>  
#>  7       4.6     3.4     1.4     0.3 setosa  <dbl>   <dbl>  
#>  8       5       3.4     1.5     0.2 setosa  <dbl>   <dbl>  
#>  9       4.4     2.9     1.4     0.2 setosa  <dbl>   <dbl>  
#> 10       4.9     3.1     1.5     0.1 setosa  <dbl>   <dbl>  
#> # … with more rows, and abbreviated variable names
#> #   ¹​Sepal_Length, ²​Sepal_Width, ³​Petal_Length,
#> #   ⁴​Petal_Width, ⁵​features_temp, ⁶​features

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_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_vector_slicer(), ft_word2vec()