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From felixcheung <...@git.apache.org>
Subject [GitHub] spark pull request #16800: [SPARK-19456][SparkR]:Add LinearSVC R API
Date Sun, 12 Feb 2017 16:03:28 GMT
Github user felixcheung commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16800#discussion_r100696204
  
    --- Diff: R/pkg/R/mllib_classification.R ---
    @@ -39,6 +46,116 @@ setClass("MultilayerPerceptronClassificationModel", representation(jobj
= "jobj"
     #' @note NaiveBayesModel since 2.0.0
     setClass("NaiveBayesModel", representation(jobj = "jobj"))
     
    +#' linear SVM Model
    +#'
    +#' Fits an linear SVM model against a SparkDataFrame. It is a binary classifier, similar
to svm in glmnet package
    +#' Users can print, make predictions on the produced model and save the model to the
input path.
    +#'
    +#' @param data SparkDataFrame for training.
    +#' @param formula A symbolic description of the model to be fitted. Currently only a
few formula
    +#'                operators are supported, including '~', '.', ':', '+', and '-'.
    +#' @param regParam The regularization parameter.
    +#' @param maxIter Maximum iteration number.
    +#' @param tol Convergence tolerance of iterations.
    +#' @param standardization Whether to standardize the training features before fitting
the model. The coefficients
    +#'                        of models will be always returned on the original scale, so
it will be transparent for
    +#'                        users. Note that with/without standardization, the models should
be always converged
    +#'                        to the same solution when no regularization is applied. Default
is TRUE, same as glmnet.
    +#' @param threshold The threshold in binary classification, in range [0, 1].
    +#' @param weightCol The weight column name.
    +#' @param ... additional arguments passed to the method.
    +#' @return \code{spark.svmLinear} returns a fitted linear SVM model.
    +#' @rdname spark.svmLinear
    +#' @aliases spark.svmLinear,SparkDataFrame,formula-method
    +#' @name spark.svmLinear
    +#' @export
    +#' @examples
    +#' \dontrun{
    +#' sparkR.session()
    +#' df <- createDataFrame(iris)
    +#' training <- df[df$Species %in% c("versicolor", "virginica"), ]
    +#' model <- spark.svmLinear(training, Species ~ ., regParam = 0.5)
    +#' summary <- summary(model)
    +#'
    +#' # fitted values on training data
    +#' fitted <- predict(model, training)
    +#'
    +#' # save fitted model to input path
    +#' path <- "path/to/model"
    +#' write.ml(model, path)
    +#'
    +#' # can also read back the saved model and predict
    +#' # Note that summary deos not work on loaded model
    +#' savedModel <- read.ml(path)
    +#' summary(savedModel)
    +#' }
    +#' @note spark.svmLinear since 2.2.0
    +setMethod("spark.svmLinear", signature(data = "SparkDataFrame", formula = "formula"),
    +          function(data, formula, regParam = 0.0, maxIter = 100, tol = 1E-6, standardization
= TRUE,
    +                   threshold = 0.5, weightCol = NULL) {
    +            formula <- paste(deparse(formula), collapse = "")
    +
    +            if (is.null(weightCol)) {
    +              weightCol <- ""
    --- End diff --
    
    I guess this is the same in spark.logit


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