ft_r_formula {sparklyr} | R Documentation |
Feature Transformation – RFormula (Estimator)
Description
Implements the transforms required for fitting a dataset against an R model
formula. Currently we support a limited subset of the R operators,
including ~
, .
, :
, +
, and -
.
Usage
ft_r_formula(
x,
formula = NULL,
features_col = "features",
label_col = "label",
force_index_label = FALSE,
uid = random_string("r_formula_"),
...
)
Arguments
x |
A |
formula |
R formula as a character string or a formula. Formula objects are converted to character strings directly and the environment is not captured. |
features_col |
Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by |
label_col |
Label column name. The column should be a numeric column. Usually this column is output by |
force_index_label |
(Spark 2.1.0+) Force to index label whether it is numeric or
string type. Usually we index label only when it is string type. If
the formula was used by classification algorithms, we can force to index
label even it is numeric type by setting this param with true.
Default: |
uid |
A character string used to uniquely identify the feature transformer. |
... |
Optional arguments; currently unused. |
Details
The basic operators in the formula are:
~ separate target and terms
+ concat terms, "+ 0" means removing intercept
- remove a term, "- 1" means removing intercept
: interaction (multiplication for numeric values, or binarized categorical values)
. all columns except target
Suppose a and b are double columns, we use the following simple examples to illustrate the effect of RFormula:
-
y ~ a + b
means modely ~ w0 + w1 * a + w2 * b
wherew0
is the intercept andw1, w2
are coefficients. -
y ~ a + b + a:b - 1
means modely ~ w1 * a + w2 * b + w3 * a * b
wherew1, w2, w3
are coefficients.
RFormula produces a vector column of features and a double or string column of label. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If the label column is of type string, it will be first transformed to double with StringIndexer. If the label column does not exist in the DataFrame, the output label column will be created from the specified response variable in the formula.
In the case where x
is a tbl_spark
, the estimator
fits against x
to obtain a transformer, returning a tbl_spark
.
Value
The object returned depends on the class of x
. If it is a
spark_connection
, the function returns a ml_estimator
or a
ml_estimator
object. If it is a ml_pipeline
, it will return
a pipeline with the transformer or estimator appended to it. If a
tbl_spark
, it will return a tbl_spark
with the transformation
applied to it.
See Also
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_one_hot_encoder_estimator()
,
ft_pca()
,
ft_polynomial_expansion()
,
ft_quantile_discretizer()
,
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()