Prepare a Spark DataFrame for Spark ML Routines


ml_prepare_dataframe(x, features, response = NULL, ..., ml.options = ml_options(), envir = new.env(parent = emptyenv()))


An object coercable to a Spark DataFrame (typically, a tbl_spark).
The name of features (terms) to use for the model fit.
The name of the response vector (as a length-one character vector), or a formula, giving a symbolic description of the model to be fitted. When response is a formula, it is used in preference to other parameters to set the response, features, and intercept parameters (if available). Currently, only simple linear combinations of existing parameters is supposed; e.g. response ~ feature1 + feature2 + .... The intercept term can be omitted by using - 1 in the model fit.
Optional arguments; currently unused.
Optional arguments, used to affect the model generated. See ml_options for more details.
An R environment -- when supplied, it will be filled with metadata describing the transformations that have taken place.


This routine prepares a Spark DataFrame for use by Spark ML routines.


Spark DataFrames are prepared through the following transformations:

  1. All specified columns are transformed into a numeric data type (using a simple cast for integer / logical columns, and ft_string_indexer for strings),
  2. The ft_vector_assembler is used to combine the specified features into a single 'feature' vector, suitable for use with Spark ML routines.

After calling this function, the envir environment (when supplied) will be populated with a set of variables:

The name of the generated features vector.
The name of the generated response vector.