Train Ratio Image CNN — train_ratio

Fits a pairwise-ratio CNN on log-scale biomarker data and returns predictions for new samples.

Usage

train_ratio_cnn(
  X_train,
  y_train,
  X_test,
  epochs = 30L,
  lr = 0.001,
  batch_size = 256L,
  class_weight = TRUE,
  device = NULL,
  verbose = TRUE,
  seed = 42L,
  positive = NULL
)

Arguments

X_train: Numeric matrix with training samples in rows and features in columns.
y_train: Binary outcome vector. Factors, characters, logical values, and integer `0/1` labels are supported.
X_test: Numeric matrix with test samples in rows and features in columns.
epochs: Number of training epochs. Defaults to `30`.
lr: Learning rate. Defaults to `0.001`.
batch_size: Mini-batch size. Defaults to `256`.
class_weight: Logical; if `TRUE`, use inverse-frequency positive-class weighting. Defaults to `TRUE`.
device: One of `"cpu"`, `"cuda"`, or `NULL`/`"auto"` for automatic selection.
verbose: Logical; print training progress. Defaults to `TRUE`.
seed: Integer random seed used for reproducible torch initialization. Defaults to `42`.
positive: Optional positive-class label for non-numeric outcomes.

Value

A list with elements `predictions`, `fit`, `model`, `train_images`, and `image_stats`.

References

Aitchison J. (1986). The Statistical Analysis of Compositional Data. Chapman and Hall.

Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barcelo-Vidal C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279-300.

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. (2017). Microbiome datasets are compositional: and this is not optional. Frontiers in Microbiology, 8, 2224.

Quinn TP, Erb I, Richardson MF, Crowley TM. (2020). Understanding sequencing data as compositions: an outlook and review. Bioinformatics, 36(16), 4424-4432.

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("torch", quietly = TRUE) &&
    requireNamespace("coro", quietly = TRUE)) {
  X_train <- matrix(rnorm(14 * 20), nrow = 20)
  y_train <- factor(rep(c("control", "case"), each = 10))
  X_test <- matrix(rnorm(14 * 5), nrow = 5)
  fit <- train_ratio_cnn(X_train, y_train, X_test, epochs = 5, verbose = FALSE)
  fit$predictions
}
} # }