sparklyr (version 0.6.4)

ml_prepare_dataframe: Prepare a Spark DataFrame for Spark ML Routines

Description

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

Usage

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

Arguments

x

An object coercable to a Spark DataFrame (typically, a tbl_spark).

features

The name of features (terms) to use for the model fit.

response

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. The data argument can be used to specify the data to be used when x is a formula; this allows calls of the form ml_linear_regression(y ~ x, data = tbl), and is especially useful in conjunction with do.

ml.options

Optional arguments, used to affect the model generated. See ml_options for more details.

envir

An R environment -- when supplied, it will be filled with metadata describing the transformations that have taken place.

Details

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:

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

Examples

# NOT RUN {
# example of how 'ml_prepare_dataframe' might be used to invoke
# Spark's LinearRegression routine from the 'ml' package
envir <- new.env(parent = emptyenv())
tdf <- ml_prepare_dataframe(df, features, response, envir = envir)

lr <- invoke_new(
  sc,
  "org.apache.spark.ml.regression.LinearRegression"
)

# use generated 'features', 'response' vector names in model fit
model <- lr %>%
  invoke("setFeaturesCol", envir$features) %>%
  invoke("setLabelCol", envir$response)
# }