ml_make_seq_model: Deterministic sequence model factory (GRU/LSTM/CNN1D with...

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Deterministic sequence model factory (GRU/LSTM/CNN1D with linear fallback)

Description

Returns fit/predict closures for sequence models that consume flattened tabular inputs (n \times (steps \times p)) and internally reshape to (n, steps, p). If Keras/TensorFlow is unavailable, falls back to a linear baseline so examples remain runnable on CPU-only machines.

Usage

ml_make_seq_model(
  type = c("linear", "gru", "lstm", "cnn1d"),
  steps = 26L,
  units = 16L,
  dense = NULL,
  dropout = 0.1,
  epochs = 12L,
  batch_size = 128L,
  lr = 0.01,
  patience = 2L,
  seed = 123L,
  deterministic = TRUE,
  pred_batch_size = 256L
)

Arguments

type	One of "linear", "gru", "lstm", "cnn1d".
steps	Integer sequence length (e.g., 26 for 6 months of weeks).
units	Hidden units for GRU/LSTM or filters for CNN1D.
dense	Optional integer vector of additional dense layers.
dropout	Dropout rate for recurrent/CNN core.
epochs, batch_size	Training settings.
lr	Learning rate.
patience	Early-stopping patience.
seed	Integer seed.
deterministic	Logical; set determinism knobs when TRUE.
pred_batch_size	Fixed batch size used at prediction time.

Details

Determinism knobs: fixed seeds, TF_DETERMINISTIC_OPS=1, no shuffle, workers=1, and a fixed pred_batch_size to minimise retracing.

Optional dependencies: keras and tensorflow. When not available, the factory returns the linear fallback.

Value

A list with ⁠$fit⁠ and ⁠$predict⁠ closures.

Examples

## Not run: 
seq_gru <- ml_make_seq_model("gru", steps = 26, units = 16, epochs = 12)

## End(Not run)

PortfolioTesteR documentation built on Nov. 5, 2025, 5:23 p.m.