SparkR (version 2.1.2)

spark.isoreg: Isotonic Regression Model

Description

Fits an Isotonic Regression model against a SparkDataFrame, similarly to R's isoreg(). Users can print, make predictions on the produced model and save the model to the input path.

Usage

spark.isoreg(data, formula, ...)
# S4 method for SparkDataFrame,formula
spark.isoreg(data, formula,
  isotonic = TRUE, featureIndex = 0, weightCol = NULL)
# S4 method for IsotonicRegressionModel
predict(object, newData)
# S4 method for IsotonicRegressionModel
summary(object)
# S4 method for IsotonicRegressionModel,character
write.ml(object, path,
  overwrite = FALSE)

Arguments

data

SparkDataFrame for training.

formula

A symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'.

...

additional arguments passed to the method.

isotonic

Whether the output sequence should be isotonic/increasing (TRUE) or antitonic/decreasing (FALSE).

featureIndex

The index of the feature if featuresCol is a vector column (default: 0), no effect otherwise.

weightCol

The weight column name.

object

a fitted IsotonicRegressionModel.

newData

SparkDataFrame for testing.

path

The directory where the model is saved.

overwrite

Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.

Value

spark.isoreg returns a fitted Isotonic Regression model.

predict returns a SparkDataFrame containing predicted values.

summary returns summary information of the fitted model, which is a list. The list includes model's boundaries (boundaries in increasing order) and predictions (predictions associated with the boundaries at the same index).

Examples

# NOT RUN {
sparkR.session()
data <- list(list(7.0, 0.0), list(5.0, 1.0), list(3.0, 2.0),
        list(5.0, 3.0), list(1.0, 4.0))
df <- createDataFrame(data, c("label", "feature"))
model <- spark.isoreg(df, label ~ feature, isotonic = FALSE)
# return model boundaries and prediction as lists
result <- summary(model, df)
# prediction based on fitted model
predict_data <- list(list(-2.0), list(-1.0), list(0.5),
                list(0.75), list(1.0), list(2.0), list(9.0))
predict_df <- createDataFrame(predict_data, c("feature"))
# get prediction column
predict_result <- collect(select(predict(model, predict_df), "prediction"))

# save fitted model to input path
path <- "path/to/model"
write.ml(model, path)

# can also read back the saved model and print
savedModel <- read.ml(path)
summary(savedModel)
# }