mlr3torch: Deep Learning with 'mlr3'

devtools::load_all()

# manually construct the task once
# library(here)
# library(data.table)
library(data.table)
withr::local_options(mlr3torch.cache = TRUE)

constructor_melanoma = function(path) {
  require_namespaces("curl")

  # should happen automatically, but this is needed for curl to work
  fs::dir_create(path, recurse = TRUE)

  base_url = "https://huggingface.co/datasets/carsonzhang/ISIC_2020_small/resolve/main/"

  compressed_tarball_file_name = "hf_ISIC_2020_small.tar.gz"
  compressed_tarball_path = file.path(path, compressed_tarball_file_name)

  curl::curl_download(url = paste0(base_url, compressed_tarball_file_name), destfile = compressed_tarball_path)
  utils::untar(compressed_tarball_path, exdir = path)
  on.exit({file.remove(compressed_tarball_path)}, add = TRUE)

  training_metadata_file_name = "ISIC_2020_Training_GroundTruth_v2.csv"
  training_metadata = fread(file.path(path, training_metadata_file_name))

  test_metadata_file_name = "ISIC_2020_Test_Metadata.csv"
  test_metadata = fread(file.path(path, test_metadata_file_name))

  training_metadata = training_metadata[, "split" := "train"]
  test_metadata = setnames(test_metadata,
    old = c("image", "patient", "anatom_site_general"),
    new = c("image_name", "patient_id", "anatom_site_general_challenge")
  )[, "split" := "test"]
  # response column needs to be filled for the test data
  metadata = rbind(training_metadata, test_metadata, fill = TRUE)
  metadata[, "image_name" := NULL]
  metadata[, "target" := NULL]
  metadata = setnames(metadata, old = "benign_malignant", new = "outcome")

  metadata
}

metadata = constructor_melanoma(path <- file.path(get_cache_dir(), "datasets", "melanoma"))

melanoma_ds_generator = torch::dataset(
  initialize = function() {
    self$.metadata = metadata
    self$.path = path
  },
  .getitem = function(idx) {
    force(idx)

    x = torchvision::base_loader(file.path(self$.path, paste0(self$.metadata[idx, ]$file_name)))
    x = torchvision::transform_to_tensor(x)

    return(list(x = x))
  },
  .length = function() {
    nrow(self$.metadata)
  }
)

melanoma_ds = melanoma_ds_generator()

dd = as_data_descriptor(melanoma_ds, list(x = c(NA, 3, 128, 128)))
lt = lazy_tensor(dd)

melanoma_dt = cbind(metadata, data.table(image = lt))

# change the encodings of variables: diagnosis, outcome
# melanoma_dt[, "outcome" := factor(get("outcome"), levels = c("benign", "malignant"))]

char_vars = c("outcome", "sex", "anatom_site_general_challenge")
melanoma_dt[, (char_vars) := lapply(.SD, factor), .SDcols = char_vars]

tsk_melanoma = as_task_classif(melanoma_dt, target = "outcome", id = "melanoma")
tsk_melanoma$set_col_roles("patient_id", "group")
tsk_melanoma$col_roles$feature = c("sex", "anatom_site_general_challenge", "age_approx", "image")

tsk_melanoma$label = "Melanoma Classification"

ci = col_info(tsk_melanoma$backend)

saveRDS(ci, here::here("inst/col_info/melanoma.rds"))