inst/doc/mlr3-integration.R

In ggmlR: 'GGML' Tensor Operations for Machine Learning

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  # Executed locally (NOT_CRAN=true); skipped on CRAN to avoid the
  # "CPU time > elapsed" vignette NOTE from the CPU fallback.
  eval     = identical(Sys.getenv("NOT_CRAN"), "true") &&
             requireNamespace("mlr3", quietly = TRUE) &&
             requireNamespace("paradox", quietly = TRUE)
)
library(ggmlR)
# Loading ggmlR installs a load hook that registers the mlr3 learners
# (classif.ggml / regr.ggml) automatically once mlr3 is loaded — no manual
# registration call is needed.
if (requireNamespace("mlr3", quietly = TRUE)) {
  library(mlr3)
}

## -----------------------------------------------------------------------------
# library(ggmlR)
# library(mlr3)
# 
# task    <- tsk("iris")
# learner <- lrn("classif.ggml",
#                epochs     = 20L,
#                batch_size = 16L,
#                predict_type = "prob")
# 
# learner$train(task)
# pred <- learner$predict(task)
# pred$score(msr("classif.acc"))

## -----------------------------------------------------------------------------
# task    <- tsk("mtcars")
# learner <- lrn("regr.ggml",
#                epochs     = 50L,
#                batch_size = 8L)
# 
# learner$train(task)
# pred <- learner$predict(task)
# pred$score(msr("regr.rmse"))

## -----------------------------------------------------------------------------
# learner <- lrn("classif.ggml")
# learner$param_set$values$epochs        <- 30L
# learner$param_set$values$hidden_layers <- c(256L, 128L, 64L)
# learner$param_set$values$dropout       <- 0.3
# learner$param_set$values$backend       <- "gpu"

## ----eval=identical(Sys.getenv("NOT_CRAN"), "true") && ggml_vulkan_available()----
# learner <- lrn("classif.ggml",
#                backend = "gpu",
#                epochs  = 100L)
# learner$train(tsk("iris"))

## -----------------------------------------------------------------------------
# learner <- lrn("classif.ggml",
#                epochs     = 50L,
#                batch_size = 16L)
# 
# learner$model_fn <- function(task, n_features, n_out, pars) {
#   ggml_model_sequential() |>
#     ggml_layer_dense(64L, activation = "relu", input_shape = n_features) |>
#     ggml_layer_dropout(rate = 0.3) |>
#     ggml_layer_dense(32L, activation = "relu") |>
#     ggml_layer_dense(n_out, activation = "softmax")
# }
# 
# learner$train(tsk("iris"))

## -----------------------------------------------------------------------------
# task    <- tsk("iris")
# learner <- lrn("classif.ggml",
#                epochs     = 20L,
#                batch_size = 16L,
#                backend    = "cpu")
# 
# rr <- resample(task, learner, rsmp("cv", folds = 5L))
# rr$aggregate(msr("classif.acc"))

## ----eval=FALSE---------------------------------------------------------------
# design <- benchmark_grid(
#   tasks    = tsk("iris"),
#   learners = list(
#     lrn("classif.ggml", epochs = 20L, batch_size = 16L, backend = "cpu"),
#     lrn("classif.ggml", epochs = 20L, batch_size = 16L, backend = "gpu")
#   ),
#   resamplings = rsmp("cv", folds = 5L)
# )
# bmr <- benchmark(design)
# bmr$aggregate(msr("classif.acc"))

## ----eval=FALSE---------------------------------------------------------------
# library(mlr3tuning)
# 
# learner <- lrn("classif.ggml", backend = "gpu")
# 
# search_space <- ps(
#   epochs     = p_int(lower = 10L, upper = 100L),
#   batch_size = p_int(lower = 8L,  upper = 64L),
#   dropout    = p_dbl(lower = 0,   upper = 0.5)
# )
# 
# instance <- ti(
#   task       = tsk("iris"),
#   learner    = learner,
#   resampling = rsmp("cv", folds = 3L),
#   measures   = msr("classif.acc"),
#   terminator = trm("evals", n_evals = 20L)
# )
# 
# tuner <- tnr("random_search")
# tuner$optimize(instance)
# 
# instance$result

## -----------------------------------------------------------------------------
# learner <- lrn("classif.ggml",
#                epochs     = 200L,
#                batch_size = 16L,
#                callbacks  = list(
#                  ggml_callback_early_stopping(
#                    monitor  = "val_loss",
#                    patience = 10L
#                  )
#                ),
#                validation_split = 0.2)
# 
# learner$train(tsk("iris"))

## -----------------------------------------------------------------------------
# d <- data.frame(
#   x1 = rnorm(100),
#   x2 = rnorm(100),
#   y  = factor(rep(c("a", "b"), each = 50)),
#   w  = c(rep(2.0, 50), rep(0.5, 50))
# )
# task <- as_task_classif(d, target = "y")
# task$set_col_roles("w", roles = "weights_learner")
# 
# learner <- lrn("classif.ggml", epochs = 20L)
# learner$train(task)

## -----------------------------------------------------------------------------
# learner <- lrn("classif.ggml", epochs = 10L, backend = "cpu")
# learner$train(tsk("iris"))
# 
# learner$marshal()
# learner$marshaled
# #> [1] TRUE
# 
# learner$unmarshal()
# learner$marshaled
# #> [1] FALSE
# 
# # Predictions are identical after roundtrip
# pred <- learner$predict(tsk("iris"))

## -----------------------------------------------------------------------------
# model <- ggml_model_sequential() |>
#   ggml_layer_dense(16L, activation = "relu", input_shape = 4L) |>
#   ggml_layer_dense(3L,  activation = "softmax")
# model <- ggml_compile(model, optimizer = "adam",
#                       loss = "categorical_crossentropy")
# 
# blob <- ggml_marshal_model(model)
# blob
# 
# model2 <- ggml_unmarshal_model(blob)