Nothing
# This file is auto-generated by h2o-3/h2o-bindings/bin/gen_R.py
# Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
#'
# -------------------------- coxph -------------------------- #
#'
#' Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset
#'
#' @param x (Optional) A vector containing the names or indices of the predictor variables to use in building the model.
#' If x is missing, then all columns except event_column, start_column and stop_column are used.
#' @param event_column The name of binary data column in the training frame indicating the occurrence of an event.
#' @param training_frame Id of the training data frame.
#' @param model_id Destination id for this model; auto-generated if not specified.
#' @param start_column Start Time Column.
#' @param stop_column Stop Time Column.
#' @param weights_column Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from
#' the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative
#' weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the
#' data frame. This is typically the number of times a row is repeated, but non-integer values are supported as
#' well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If
#' you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get
#' an accurate prediction, remove all rows with weight == 0.
#' @param offset_column Offset column. This will be added to the combination of columns before applying the link function.
#' @param stratify_by List of columns to use for stratification.
#' @param ties Method for Handling Ties. Must be one of: "efron", "breslow". Defaults to efron.
#' @param init Coefficient starting value. Defaults to 0.
#' @param lre_min Minimum log-relative error. Defaults to 9.
#' @param max_iterations Maximum number of iterations. Defaults to 20.
#' @param interactions A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
#' @param interaction_pairs A list of pairwise (first order) column interactions.
#' @param interactions_only A list of columns that should only be used to create interactions but should not itself participate in model
#' training.
#' @param use_all_factor_levels \code{Logical}. (Internal. For development only!) Indicates whether to use all factor levels. Defaults to
#' FALSE.
#' @param export_checkpoints_dir Automatically export generated models to this directory.
#' @param single_node_mode \code{Logical}. Run on a single node to reduce the effect of network overhead (for smaller datasets) Defaults
#' to FALSE.
#' @examples
#' \dontrun{
#' library(h2o)
#' h2o.init()
#'
#' # Import the heart dataset
#' f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv"
#' heart <- h2o.importFile(f)
#'
#' # Set the predictor and response
#' predictor <- "age"
#' response <- "event"
#'
#' # Train a Cox Proportional Hazards model
#' heart_coxph <- h2o.coxph(x = predictor, training_frame = heart,
#' event_column = "event",
#' start_column = "start",
#' stop_column = "stop")
#' }
#' @export
h2o.coxph <- function(x,
event_column,
training_frame,
model_id = NULL,
start_column = NULL,
stop_column = NULL,
weights_column = NULL,
offset_column = NULL,
stratify_by = NULL,
ties = c("efron", "breslow"),
init = 0,
lre_min = 9,
max_iterations = 20,
interactions = NULL,
interaction_pairs = NULL,
interactions_only = NULL,
use_all_factor_levels = FALSE,
export_checkpoints_dir = NULL,
single_node_mode = FALSE)
{
# Validate required training_frame first and other frame args: should be a valid key or an H2OFrame object
training_frame <- .validate.H2OFrame(training_frame, required=TRUE)
# Validate other required args
# If x is missing, then assume user wants to use all columns as features.
if (missing(x)) {
if (is.numeric(event_column)) {
x <- setdiff(col(training_frame), event_column)
} else {
x <- setdiff(colnames(training_frame), event_column)
}
}
# Validate other args
if (is.null(interactions_only) && (! is.null(interactions) || ! is.null(interaction_pairs))) {
used <- unique(c(interactions, unlist(sapply(interaction_pairs, function(x) {x[1]})), unlist(sapply(interaction_pairs, function(x) {x[2]}))))
interactions_only <- setdiff(used, x)
x <- c(x, interactions_only)
}
if (! is.null(stratify_by)) {
stratify_by_only <- setdiff(stratify_by, x)
x <- c(x, stratify_by_only)
}
if(!is.character(stop_column) && !is.numeric(stop_column)) {
stop('argument "stop_column" must be a column name or an index')
}
# Build parameter list to send to model builder
parms <- list()
parms$training_frame <- training_frame
args <- .verify_dataxy(training_frame, x, event_column)
if( !missing(offset_column) && !is.null(offset_column)) args$x_ignore <- args$x_ignore[!( offset_column == args$x_ignore )]
if( !missing(weights_column) && !is.null(weights_column)) args$x_ignore <- args$x_ignore[!( weights_column == args$x_ignore )]
parms$ignored_columns <- args$x_ignore
parms$response_column <- args$y
if (!missing(model_id))
parms$model_id <- model_id
if (!missing(start_column))
parms$start_column <- start_column
if (!missing(stop_column))
parms$stop_column <- stop_column
if (!missing(weights_column))
parms$weights_column <- weights_column
if (!missing(offset_column))
parms$offset_column <- offset_column
if (!missing(stratify_by))
parms$stratify_by <- stratify_by
if (!missing(ties))
parms$ties <- ties
if (!missing(init))
parms$init <- init
if (!missing(lre_min))
parms$lre_min <- lre_min
if (!missing(max_iterations))
parms$max_iterations <- max_iterations
if (!missing(interactions))
parms$interactions <- interactions
if (!missing(interaction_pairs))
parms$interaction_pairs <- interaction_pairs
if (!missing(interactions_only))
parms$interactions_only <- interactions_only
if (!missing(use_all_factor_levels))
parms$use_all_factor_levels <- use_all_factor_levels
if (!missing(export_checkpoints_dir))
parms$export_checkpoints_dir <- export_checkpoints_dir
if (!missing(single_node_mode))
parms$single_node_mode <- single_node_mode
# Error check and build model
model <- .h2o.modelJob('coxph', parms, h2oRestApiVersion=3, verbose=FALSE)
return(model)
}
.h2o.train_segments_coxph <- function(x,
event_column,
training_frame,
start_column = NULL,
stop_column = NULL,
weights_column = NULL,
offset_column = NULL,
stratify_by = NULL,
ties = c("efron", "breslow"),
init = 0,
lre_min = 9,
max_iterations = 20,
interactions = NULL,
interaction_pairs = NULL,
interactions_only = NULL,
use_all_factor_levels = FALSE,
export_checkpoints_dir = NULL,
single_node_mode = FALSE,
segment_columns = NULL,
segment_models_id = NULL,
parallelism = 1)
{
# formally define variables that were excluded from function parameters
model_id <- NULL
verbose <- NULL
destination_key <- NULL
# Validate required training_frame first and other frame args: should be a valid key or an H2OFrame object
training_frame <- .validate.H2OFrame(training_frame, required=TRUE)
# Validate other required args
# If x is missing, then assume user wants to use all columns as features.
if (missing(x)) {
if (is.numeric(event_column)) {
x <- setdiff(col(training_frame), event_column)
} else {
x <- setdiff(colnames(training_frame), event_column)
}
}
# Validate other args
if (is.null(interactions_only) && (! is.null(interactions) || ! is.null(interaction_pairs))) {
used <- unique(c(interactions, unlist(sapply(interaction_pairs, function(x) {x[1]})), unlist(sapply(interaction_pairs, function(x) {x[2]}))))
interactions_only <- setdiff(used, x)
x <- c(x, interactions_only)
}
if (! is.null(stratify_by)) {
stratify_by_only <- setdiff(stratify_by, x)
x <- c(x, stratify_by_only)
}
if(!is.character(stop_column) && !is.numeric(stop_column)) {
stop('argument "stop_column" must be a column name or an index')
}
# Build parameter list to send to model builder
parms <- list()
parms$training_frame <- training_frame
args <- .verify_dataxy(training_frame, x, event_column)
if( !missing(offset_column) && !is.null(offset_column)) args$x_ignore <- args$x_ignore[!( offset_column == args$x_ignore )]
if( !missing(weights_column) && !is.null(weights_column)) args$x_ignore <- args$x_ignore[!( weights_column == args$x_ignore )]
parms$ignored_columns <- args$x_ignore
parms$response_column <- args$y
if (!missing(start_column))
parms$start_column <- start_column
if (!missing(stop_column))
parms$stop_column <- stop_column
if (!missing(weights_column))
parms$weights_column <- weights_column
if (!missing(offset_column))
parms$offset_column <- offset_column
if (!missing(stratify_by))
parms$stratify_by <- stratify_by
if (!missing(ties))
parms$ties <- ties
if (!missing(init))
parms$init <- init
if (!missing(lre_min))
parms$lre_min <- lre_min
if (!missing(max_iterations))
parms$max_iterations <- max_iterations
if (!missing(interactions))
parms$interactions <- interactions
if (!missing(interaction_pairs))
parms$interaction_pairs <- interaction_pairs
if (!missing(interactions_only))
parms$interactions_only <- interactions_only
if (!missing(use_all_factor_levels))
parms$use_all_factor_levels <- use_all_factor_levels
if (!missing(export_checkpoints_dir))
parms$export_checkpoints_dir <- export_checkpoints_dir
if (!missing(single_node_mode))
parms$single_node_mode <- single_node_mode
# Build segment-models specific parameters
segment_parms <- list()
if (!missing(segment_columns))
segment_parms$segment_columns <- segment_columns
if (!missing(segment_models_id))
segment_parms$segment_models_id <- segment_models_id
segment_parms$parallelism <- parallelism
# Error check and build segment models
segment_models <- .h2o.segmentModelsJob('coxph', segment_parms, parms, h2oRestApiVersion=3)
return(segment_models)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.