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#'
#' H2O Segmented-Data Bulk Model Training
#'
#' Provides a set of functions to train a group of models on different
#' segments (subpopulations) of the training set.
#--------------------------------------------
# Segmented-data bulk model training function
#--------------------------------------------
#'
#' Start Segmented-Data bulk Model Training for a given algorithm and parameters.
#'
#' @param algorithm Name of algorithm to use in training segment models (gbm, randomForest, kmeans, glm, deeplearning, naivebayes, psvm,
#' xgboost, pca, svd, targetencoder, aggregator, word2vec, coxph, isolationforest, kmeans, stackedensemble, glrm, gam, anovaglm, modelselection).
#' @param segment_columns A list of columns to segment-by. H2O will group the training (and validation) dataset by the segment-by columns
#' and train a separate model for each segment (group of rows).
#' @param segment_models_id Identifier for the returned collection of Segment Models. If not specified it will be automatically generated.
#' @param parallelism Level of parallelism of bulk model building, it is the maximum number of models each H2O node will be building in parallel, defaults to 1.
#' @param ... Use to pass along training_frame parameter, x, y, and all non-default parameter values to the algorithm
# (i.e., balance_classes, ntrees, alpha).
#' Look at the specific algorithm - h2o.gbm, h2o.glm, h2o.kmeans, h2o.deepLearning - for available parameters.
#' @examples
#' \dontrun{
#' library(h2o)
#' h2o.init()
#' iris_hf <- as.h2o(iris)
#' models <- h2o.train_segments(algorithm = "gbm",
#' segment_columns = "Species",
#' x = c(1:3), y = 4,
#' training_frame = iris_hf,
#' ntrees = 5,
#' max_depth = 4)
#' as.data.frame(models)
#' }
#' @export
h2o.train_segments <- function(algorithm,
segment_columns,
segment_models_id,
parallelism = 1,
...)
{
train_segments_fun_name <- sprintf(".h2o.train_segments_%s", tolower(algorithm))
if (!exists(train_segments_fun_name)) {
stop(sprintf("Algorithm %s is not recognized, please check the spelling. For the name to be valid, a function h2o.%s needs to exist as well).", algorithm, algorithm))
}
params <- list(...)
if (!missing(segment_columns))
params$segment_columns <- segment_columns
if (!missing(segment_models_id))
params$segment_models_id <- segment_models_id
params$parallelism <- parallelism
return(do.call(train_segments_fun_name, args = params))
}
#' @rdname H2OSegmentModels-class
#' @param object an \code{H2OModel} object.
#' @export
setMethod("show", "H2OSegmentModels",
function(object) {
cat("Segment Models ID:", object@segment_models_id, "\n")
cat("Individual Segment Models:\n")
df <- as.data.frame(object)
print(df)
invisible(object)
})
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