ft_r_formula

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Feature Transformation -- RFormula (Estimator)

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 -. Also see the R formula docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html

Usage
ft_r_formula(x, formula = NULL, features_col = "features",
  label_col = "label", force_index_label = FALSE, dataset = NULL,
  uid = random_string("r_formula_"), ...)
Arguments
x

A spark_connection, ml_pipeline, or a tbl_spark.

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 ft_r_formula.

label_col

Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula.

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: FALSE.

dataset

(Optional) A tbl_spark. If provided, eagerly fit the (estimator) feature "transformer" against dataset. See details.

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 model y ~ w0 + w1 * a + w2 * b where w0 is the intercept and w1, w2 are coefficients.

  • y ~ a + b + a:b - 1 means model y ~ w1 * a + w2 * b + w3 * a * b where w1, 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.

When dataset is provided for an estimator transformer, the function internally calls ml_fit() against dataset. Hence, the methods for spark_connection and ml_pipeline will then return a ml_transformer and a ml_pipeline with a ml_transformer appended, respectively. When x is a tbl_spark, the estimator will be fit against dataset before transforming x.

When dataset is not specified, the constructor returns a ml_estimator, and, in the case where x is a tbl_spark, the estimator fits against x then to obtain a transformer, which is then immediately used to transform x.

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

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_regex_tokenizer, 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

Aliases
  • ft_r_formula
Documentation reproduced from package sparklyr, version 0.9.4, License: Apache License 2.0 | file LICENSE

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