setup_SuperConfig: Setup SuperConfig

In rtemis: Schema-Driven Machine Learning and Visualization

Setup SuperConfig

Description

Setup SuperConfig object. SuperConfig is a portable, data-agnostic recipe: dat_training_path is optional, so the same config can be validated, shared, or described without data and have a path bound later (e.g. by the rtemis CLI) before train.

Usage

setup_SuperConfig(
  dat_training_path = NULL,
  dat_validation_path = NULL,
  dat_test_path = NULL,
  character2factor = TRUE,
  outcome = NULL,
  features = NULL,
  weights = NULL,
  positive_class = NULL,
  preprocessor_config = NULL,
  decomposition_config = NULL,
  hyperparameters = NULL,
  tuner_config = NULL,
  outer_resampling_config = NULL,
  execution_config = setup_FutureExecution(),
  question = NULL,
  outdir = "results/",
  verbosity = 1L
)

Arguments

dat_training_path	Character or NULL: Path to training data file. NULL leaves the recipe unbound; set it (or supply data) before train.
dat_validation_path	Optional Character: Path to validation data file.
dat_test_path	Optional Character: Path to test data file.
character2factor	Logical: If TRUE, read character columns as factors. A delimited file carries no type information, so this is what decides whether a column of labels is a categorical predictor or an unusable string one. Applied to all three datasets.
outcome	Optional Character: Name of the outcome column; NULL for rtemis's convention, the last column.
features	Optional Character vector: Names of the predictor columns; NULL for every column except the outcome.
weights	Optional Character: Column name in training data to use as observation weights. If NULL, no weights are used.
positive_class	Character or NULL: For binary classification, the outcome level to treat as positive. NULL keeps the existing factor level order.
preprocessor_config	Optional SupervisedPreprocessorConfig: Configuration for data preprocessing.
decomposition_config	Optional DecompositionConfig: Configuration for data decomposition.
hyperparameters	Optional Hyperparameters or HyperparametersSet: One model configuration or a named set of configurations searched as a union.
tuner_config	Optional TunerConfig: Configuration for hyperparameter tuning.
outer_resampling_config	Optional ResamplerConfig: Configuration for outer res resampling during model training.
execution_config	ExecutionConfig object: Configuration for execution settings. Setup with setup_SerialExecution, setup_FutureExecution or setup_MiraiExecution.
question	Optional Character: Question to answer with the supervised learning analysis.
outdir	Optional Character: Output directory for results; NULL to write nothing to disk.
verbosity	Integer [0, Inf): Verbosity level.

Value

SuperConfigPaths object.

Author(s)

EDG

Examples

## Not run: 
# requires local data file
sc <- setup_SuperConfig(
  dat_training_path = "train.csv",
  preprocessor_config = setup_SupervisedPreprocessor(scale = TRUE),
  hyperparameters = setup_LightRF(),
  tuner_config = setup_GridSearch(),
  outer_resampling_config = setup_KFold(),
  execution_config = setup_FutureExecution(),
  question = "Can we tell iris species apart given their measurements?",
  outdir = "models/"
)

## End(Not run)

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.