diff options
Diffstat (limited to 'R/pkg/R/mllib.R')
| -rw-r--r-- | R/pkg/R/mllib.R | 23 |
1 files changed, 10 insertions, 13 deletions
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index 074e9cbebe..632e4add64 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -733,7 +733,6 @@ setMethod("predict", signature(object = "KMeansModel"), #' excepting that at most one value may be 0. The class with largest value p/t is predicted, where p #' is the original probability of that class and t is the class's threshold. #' @param weightCol The weight column name. -#' @param probabilityCol column name for predicted class conditional probabilities. #' @param ... additional arguments passed to the method. #' @return \code{spark.logit} returns a fitted logistic regression model #' @rdname spark.logit @@ -772,7 +771,7 @@ setMethod("predict", signature(object = "KMeansModel"), setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula"), function(data, formula, regParam = 0.0, elasticNetParam = 0.0, maxIter = 100, tol = 1E-6, family = "auto", standardization = TRUE, - thresholds = 0.5, weightCol = NULL, probabilityCol = "probability") { + thresholds = 0.5, weightCol = NULL) { formula <- paste(deparse(formula), collapse = "") if (is.null(weightCol)) { @@ -784,7 +783,7 @@ setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula") as.numeric(elasticNetParam), as.integer(maxIter), as.numeric(tol), as.character(family), as.logical(standardization), as.array(thresholds), - as.character(weightCol), as.character(probabilityCol)) + as.character(weightCol)) new("LogisticRegressionModel", jobj = jobj) }) @@ -1425,7 +1424,7 @@ setMethod("predict", signature(object = "GaussianMixtureModel"), #' @param userCol column name for user ids. Ids must be (or can be coerced into) integers. #' @param itemCol column name for item ids. Ids must be (or can be coerced into) integers. #' @param rank rank of the matrix factorization (> 0). -#' @param reg regularization parameter (>= 0). +#' @param regParam regularization parameter (>= 0). #' @param maxIter maximum number of iterations (>= 0). #' @param nonnegative logical value indicating whether to apply nonnegativity constraints. #' @param implicitPrefs logical value indicating whether to use implicit preference. @@ -1464,21 +1463,21 @@ setMethod("predict", signature(object = "GaussianMixtureModel"), #' #' # set other arguments #' modelS <- spark.als(df, "rating", "user", "item", rank = 20, -#' reg = 0.1, nonnegative = TRUE) +#' regParam = 0.1, nonnegative = TRUE) #' statsS <- summary(modelS) #' } #' @note spark.als since 2.1.0 setMethod("spark.als", signature(data = "SparkDataFrame"), function(data, ratingCol = "rating", userCol = "user", itemCol = "item", - rank = 10, reg = 0.1, maxIter = 10, nonnegative = FALSE, + rank = 10, regParam = 0.1, maxIter = 10, nonnegative = FALSE, implicitPrefs = FALSE, alpha = 1.0, numUserBlocks = 10, numItemBlocks = 10, checkpointInterval = 10, seed = 0) { if (!is.numeric(rank) || rank <= 0) { stop("rank should be a positive number.") } - if (!is.numeric(reg) || reg < 0) { - stop("reg should be a nonnegative number.") + if (!is.numeric(regParam) || regParam < 0) { + stop("regParam should be a nonnegative number.") } if (!is.numeric(maxIter) || maxIter <= 0) { stop("maxIter should be a positive number.") @@ -1486,7 +1485,7 @@ setMethod("spark.als", signature(data = "SparkDataFrame"), jobj <- callJStatic("org.apache.spark.ml.r.ALSWrapper", "fit", data@sdf, ratingCol, userCol, itemCol, as.integer(rank), - reg, as.integer(maxIter), implicitPrefs, alpha, nonnegative, + regParam, as.integer(maxIter), implicitPrefs, alpha, nonnegative, as.integer(numUserBlocks), as.integer(numItemBlocks), as.integer(checkpointInterval), as.integer(seed)) new("ALSModel", jobj = jobj) @@ -1684,8 +1683,6 @@ print.summary.KSTest <- function(x, ...) { #' nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching #' can speed up training of deeper trees. Users can set how often should the #' cache be checkpointed or disable it by setting checkpointInterval. -#' @param probabilityCol column name for predicted class conditional probabilities, only for -#' classification. #' @param ... additional arguments passed to the method. #' @aliases spark.randomForest,SparkDataFrame,formula-method #' @return \code{spark.randomForest} returns a fitted Random Forest model. @@ -1720,7 +1717,7 @@ setMethod("spark.randomForest", signature(data = "SparkDataFrame", formula = "fo maxDepth = 5, maxBins = 32, numTrees = 20, impurity = NULL, featureSubsetStrategy = "auto", seed = NULL, subsamplingRate = 1.0, minInstancesPerNode = 1, minInfoGain = 0.0, checkpointInterval = 10, - maxMemoryInMB = 256, cacheNodeIds = FALSE, probabilityCol = "probability") { + maxMemoryInMB = 256, cacheNodeIds = FALSE) { type <- match.arg(type) formula <- paste(deparse(formula), collapse = "") if (!is.null(seed)) { @@ -1749,7 +1746,7 @@ setMethod("spark.randomForest", signature(data = "SparkDataFrame", formula = "fo impurity, as.integer(minInstancesPerNode), as.numeric(minInfoGain), as.integer(checkpointInterval), as.character(featureSubsetStrategy), seed, - as.numeric(subsamplingRate), as.character(probabilityCol), + as.numeric(subsamplingRate), as.integer(maxMemoryInMB), as.logical(cacheNodeIds)) new("RandomForestClassificationModel", jobj = jobj) } |