fit_gazepoint_deep_model: Fit an optional governed deep-learning model through keras3

In gp3ml: Governance-First Predictive Modelling for 'Gazepoint' Research

Fit an optional governed deep-learning model through keras3

Description

Fit an optional governed deep-learning model through keras3

Usage

fit_gazepoint_deep_model(
  data,
  task,
  predictors = NULL,
  preprocessor = NULL,
  hidden_units = c(64L, 32L),
  dropout = 0.2,
  epochs = 50L,
  batch_size = 32L,
  validation_split = 0.2,
  optimizer = "adam",
  seed = 1L,
  verbose = 0L
)

Arguments

data	Analysis data used to fit the network.
task	A governed gp3ml_task object.
predictors	Optional character vector of predictor columns.
preprocessor	Optional fitted preprocessing object.
hidden_units	Integer vector of hidden-layer sizes.
dropout	Dropout proportion applied after hidden layers.
epochs	Number of training epochs.
batch_size	Training batch size.
validation_split	Proportion reserved for internal validation.
optimizer	Keras optimizer name or object.
seed	Deterministic random seed.
verbose	Keras training verbosity.

Value

A governed gp3ml_model object containing the fitted keras3 model, training history, preprocessing object, task contract, and training metadata.

Examples

example_data <- data.frame(
  participant_id = rep(sprintf("P%02d", 1:12), each = 2),
  trial_id = sprintf("T%02d", 1:24),
  stimulus_id = rep(c("S01", "S02"), 12),
  condition = rep(c("A", "B"), 12),
  fixation_duration = 180 + seq_len(24),
  pupil_change = sin(seq_len(24) / 3),
  stringsAsFactors = FALSE
)
example_data$quality_status <- factor(
  c(
    "pass", "review", "pass", "review", "review", "pass",
    "review", "pass", "pass", "review", "review", "pass",
    "review", "pass", "review", "pass", "pass", "review",
    "pass", "review", "review", "pass", "pass", "review"
  ),
  levels = c("pass", "review")
)
task <- declare_gazepoint_task(
  data = example_data,
  outcome = "quality_status",
  purpose = "Predict predefined recording-quality review status",
  task_type = "classification",
  unit_id = "trial_id",
  participant_id = "participant_id",
  stimulus_id = "stimulus_id",
  generalization_target = "new_participants",
  positive = "review"
)
deep_model <- fit_gazepoint_deep_model(
  data = example_data,
  task = task,
  predictors = c("fixation_duration", "pupil_change"),
  hidden_units = 4L,
  dropout = 0,
  epochs = 1L,
  batch_size = 8L,
  validation_split = 0,
  seed = 101L,
  verbose = 0L
)
deep_model

gp3ml documentation built on Aug. 23, 2026, 5:11 p.m.