ft_min_max_scaler
Feature Transformation -- MinMaxScaler (Estimator)
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
- x
A
spark_connection
,ml_pipeline
, or atbl_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
: Whenx
is aspark_connection
, the function returns aml_transformer
, aml_estimator
, or one of their subclasses. The object contains a pointer to a SparkTransformer
orEstimator
object and can be used to composePipeline
objects.ml_pipeline
: Whenx
is aml_pipeline
, the function returns aml_pipeline
with the transformer or estimator appended to the pipeline.tbl_spark
: Whenx
is atbl_spark
, a transformer is constructed then immediately applied to the inputtbl_spark
, returning atbl_spark
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_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()
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_min_max_scaler(
input_col = "features_temp",
output_col = "features"
)
# }
# NOT RUN {
# }