#' Parse the h2o cv grid into something less ridiculous.
#' @import stringr
#' @import dplyr
#' @export
#' @param grid H2OGrid of H2ORegressionModel objects
#' @return tibble of cv performance ordered by lambda
h2o_cvdf <- function(grid) {
grid@summary_table %>%
mutate(lambda = as.numeric(str_extract(lambda, "[0-9|\\.]+")),
model_ids = as.numeric(str_extract(model_ids, "[0-9]+$")),
residual_deviance = as.numeric(residual_deviance)) %>%
arrange(lambda)
}
#' Plot the CV error vis-a-vis plot.cv.glmnet. Except without standard
#' errors, because h2o doesn't store them?
#' @import ggplot2
#' @export
#' @param sane_grid Output of h2o_cvdf
#' @return ggplot object
h2o_plotcv <- function(sane_grid) {
ggplot(sane_grid, aes(x = lambda, y = residual_deviance)) +
geom_line() + geom_point() +
scale_x_log10()
}
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