Factorization Machines Regression Model

spark.fmRegressor fits a factorization regression model against a SparkDataFrame. Users can call summary to print a summary of the fitted model, predict to make predictions on new data, and write.ml/read.ml to save/load fitted models.

Usage

spark.fmRegressor(data, formula, ...)

# S4 method for class 'SparkDataFrame,formula'
spark.fmRegressor(
  data,
  formula,
  factorSize = 8,
  fitLinear = TRUE,
  regParam = 0,
  miniBatchFraction = 1,
  initStd = 0.01,
  maxIter = 100,
  stepSize = 1,
  tol = 1e-06,
  solver = c("adamW", "gd"),
  seed = NULL,
  stringIndexerOrderType = c("frequencyDesc", "frequencyAsc", "alphabetDesc",
    "alphabetAsc")
)

# S4 method for class 'FMRegressionModel'
summary(object)

# S4 method for class 'FMRegressionModel'
predict(object, newData)

# S4 method for class 'FMRegressionModel,character'
write.ml(object, path, overwrite = FALSE)

Arguments

data

a SparkDataFrame of observations and labels for model fitting.

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.

factorSize

dimensionality of the factors.

fitLinear

whether to fit linear term. # TODO Can we express this with formula?

regParam

the regularization parameter.

miniBatchFraction

the mini-batch fraction parameter.

initStd

the standard deviation of initial coefficients.

maxIter

maximum iteration number.

stepSize

stepSize parameter.

tol

convergence tolerance of iterations.

solver

solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".

seed

seed parameter for weights initialization.

stringIndexerOrderType

how to order categories of a string feature column. This is used to decide the base level of a string feature as the last category after ordering is dropped when encoding strings. Supported options are "frequencyDesc", "frequencyAsc", "alphabetDesc", and "alphabetAsc". The default value is "frequencyDesc". When the ordering is set to "alphabetDesc", this drops the same category as R when encoding strings.

object

a FM Regression Model model fitted by spark.fmRegressor.

newData

a 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.fmRegressor returns a fitted Factorization Machines Regression Model.

summary returns summary information of the fitted model, which is a list.

predict returns the predicted values based on an FMRegressionModel.

Note

spark.fmRegressor since 3.1.0

summary(FMRegressionModel) since 3.1.0

predict(FMRegressionModel) since 3.1.0

write.ml(FMRegressionModel, character) since 3.1.0

See also

read.ml

Examples

if (FALSE) { # \dontrun{
df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")

# fit Factorization Machines Regression Model
model <- spark.fmRegressor(
  df, label ~ features,
  regParam = 0.01, maxIter = 10, fitLinear = TRUE
)

# get the summary of the model
summary(model)

# make predictions
predictions <- predict(model, df)

# save and load the model
path <- "path/to/model"
write.ml(model, path)
savedModel <- read.ml(path)
summary(savedModel)
} # }