spark.fmClassifier {SparkR}

R Documentation

Factorization Machines Classification Model

Description

spark.fmClassifier fits a factorization classification 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. Only categorical data is supported.

Usage

spark.fmClassifier(data, formula, ...)

## S4 method for signature 'SparkDataFrame,formula'
spark.fmClassifier(
  data,
  formula,
  factorSize = 8,
  fitLinear = TRUE,
  regParam = 0,
  miniBatchFraction = 1,
  initStd = 0.01,
  maxIter = 100,
  stepSize = 1,
  tol = 1e-06,
  solver = c("adamW", "gd"),
  thresholds = NULL,
  seed = NULL,
  handleInvalid = c("error", "keep", "skip")
)

## S4 method for signature 'FMClassificationModel'
summary(object)

## S4 method for signature 'FMClassificationModel'
predict(object, newData)

## S4 method for signature 'FMClassificationModel,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".
thresholds	in binary classification, in range [0, 1]. If the estimated probability of class label 1 is > threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often. Note: Setting this with threshold p is equivalent to setting thresholds c(1-p, p).
seed	seed parameter for weights initialization.
handleInvalid	How to handle invalid data (unseen labels or NULL values) in features and label column of string type. Supported options: "skip" (filter out rows with invalid data), "error" (throw an error), "keep" (put invalid data in a special additional bucket, at index numLabels). Default is "error".
object	a FM Classification model fitted by spark.fmClassifier.
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.fmClassifier returns a fitted Factorization Machines Classification Model.

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

predict returns the predicted values based on a FM Classification model.

Note

spark.fmClassifier since 3.1.0

summary(FMClassificationModel) since 3.1.0

predict(FMClassificationModel) since 3.1.0

write.ml(FMClassificationModel, character) since 3.1.0

See Also

read.ml

Examples

## Not run: 
##D df <- read.df("data/mllib/sample_binary_classification_data.txt", source = "libsvm")
##D 
##D # fit Factorization Machines Classification Model
##D model <- spark.fmClassifier(
##D            df, label ~ features,
##D            regParam = 0.01, maxIter = 10, fitLinear = TRUE
##D          )
##D 
##D # get the summary of the model
##D summary(model)
##D 
##D # make predictions
##D predictions <- predict(model, df)
##D 
##D # save and load the model
##D path <- "path/to/model"
##D write.ml(model, path)
##D savedModel <- read.ml(path)
##D summary(savedModel)
## End(Not run)

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